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   Table of Contents      
RESEARCH METHDOLOGY
Year : 2008  |  Volume : 56  |  Issue : 1  |  Page : 45-50

Understanding and using sensitivity, specificity and predictive values

Rajul Parikh, Annie Mathai, Shefali Parikh, G Chandra Sekhar, Ravi Thomas
L. V. Prasad Eye Institute, Banjara Hills, Hyderabad, India

Date of Web Publication21-Dec-2007

Correspondence Address:
Rajul Parikh
Victor villa, 5, Babulnath Road, Mumbai - 400007
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0301-4738.37595

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  Abstract 

In this article, we have discussed the basic knowledge to calculate sensitivity, specificity, positive predictive value and negative predictive value. We have discussed the advantage and limitations of these measures and have provided how we should use these measures in our day-to-day clinical practice. We also have illustrated how to calculate sensitivity and specificity while combining two tests and how to use these results for our patients in day-to-day practice.

Keywords: Predictive values, sensitivity, specificity


How to cite this article:
Parikh R, Mathai A, Parikh S, Chandra Sekhar G, Thomas R. Understanding and using sensitivity, specificity and predictive values. Indian J Ophthalmol 2008;56:45-50

How to cite this URL:
Parikh R, Mathai A, Parikh S, Chandra Sekhar G, Thomas R. Understanding and using sensitivity, specificity and predictive values. Indian J Ophthalmol [serial online] 2008 [cited 2022 Sep 29];56:45-50. Available from: https://www.ijo.in/text.asp?2008/56/1/45/37595

Shows sensitivity, speciÞ city of intraocular pressure,
torch light test and van Herick test

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Shows sensitivity, speciÞ city of intraocular pressure,
torch light test and van Herick test

Click here to view
Table 4a: Showing example of calculation of predictive value
at 50% prevalence
Table 4b: Showing example of calculation of predictive
values at 1% prevalence

Click here to view
Table 4a: Showing example of calculation of predictive value
at 50% prevalence
Table 4b: Showing example of calculation of predictive
values at 1% prevalence

Click here to view
Shows example for the calculation of sensitivity and
speciÞ city

Click here to view
Shows example for the calculation of sensitivity and
speciÞ city

Click here to view
Calculation of sensitivity and speciÞ city

Click here to view
Calculation of sensitivity and speciÞ city

Click here to view
Shows 2 × 2 (two-by-two) table

Click here to view
Shows 2 × 2 (two-by-two) table

Click here to view
Modern ophthalmology has experienced a dramatic increase in knowledge and an exponential increase in technology. A lot of this 'hi-tech' explosion involves diagnostic tests. Regrettably, there is sometimes a tendency to use tests just because they are available; or because they are hi-tech. The basic idea of performing a diagnostic test is to increase (or decrease) our suspicion that a patient has a particular disease, to the extent that we can make management decisions. In this article, we have tried to explain the rationale behind tests and their 'scientific' application in the practical management of a patient.


  Diagnostic Tests Top


For this article, the term 'diagnostic tests' will include everything physicians do to diagnose disease. This includes assessing symptoms and signs, as well as what we conventionally refer to as tests: such as laboratory investigations, gonioscopy, Optical Coherence Tomography (OCT), etc.


  Gold Standard Top


The gold standard is the best single test (or a combination of tests) that is considered the current preferred method of diagnosing a particular disease (X). All other methods of diagnosing X, including any new test, need to be compared against this 'gold' standard. The gold standard is different for different diseases. If we are considering peripheral anterior chamber depth (van Herick test [2]) for the diagnosis of primary angle closure (PAC), the current gold standard is gonioscopy. The gold standard for demonstrating the functional defect in glaucoma is automated perimetry. The gold standard for X may be considered outdated or inadequate, but any new test designed to replace the gold standard has to be initially validated against the gold standard. If the new test is indeed better, there are ways to prove that; following which the new test may become the gold standard.


  Validity Top


It is the extent to which a test measures what it is supposed to measure; in other words, it is the accuracy of the test. Validity is measured by sensitivity and specificity. These terms, as well as other jargon, are best illustrated using a conventional two-by-two (2 × 2) table.

The information obtained by comparing a new diagnostic test with the gold standard is conventionally summarized in a two-by-two table [Table - 1].

In cell 'a,' we enter those in whom the test in question correctly diagnosed the disease (as determined by the gold standard). In other words, the test is positive, as is the gold standard. These are the true positives (TP).

In cell 'b,' we enter those who have positive results for the test in question but do not have disease according to the 'gold standard test.' The newer test has wrongly diagnosed the disease: These are false positives (FP).

In cell 'c,' we enter those who have disease on the 'gold standard test' but have negative results with the test in question. The test has wrongly labeled a diseased person as 'normal.' These are false negatives (FN).

In cell 'd,' we enter those who have no disease as determined by the 'gold standard test' and are also negative with the newer test. These are true negatives (TN).

Sensitivity (positive in disease)

Sensitivity is the ability of a test to correctly classify an individual as 'diseased' [Table - 2].

Sensitivity = a / a+c

= a (true positive) / a+c (true positive + false negative)

= Probability of being test positive when disease present.

Example: One hundred persons with primary angle closure glaucoma (PACG, diagnosed by 'gold standard': gonioscopy) are examined by van Herick test. Seventy-five of them had narrow peripheral anterior chamber depth [Table - 3]. The sensitivity of the peripheral anterior chamber depth examination to PACG is therefore –

75 / 100 = 75%.

Specificity (negative in health)

The ability of a test to correctly classify an individual as disease-free is called the test's specificity. [Table - 2]

Specificity = d / b+d

= d (true negative) / b+d (true negative + false positive)

= Probability of being test negative when disease absent.

Example: One hundred persons with normal angles (diagnosed by 'gold standard': gonioscopy) are examined by peripheral angle chamber depth examination. Eighty-five persons had normal peripheral angle chamber depth [Table - 3]. The specificity of the peripheral angle chamber depth examination to PACG is therefore –

85 / 100 = 85%.

Sensitivity and specificity are inversely proportional, meaning that as the sensitivity increases, the specificity decreases and vice versa. What do we mean by this? Let us say that an intraocular pressure (IOP) of =25 mmHg is test positive and <25 mmHg is test negative. Very few normal subjects would have IOP more than 25 mmHg, and hence the specificity (NIH – negative in health) would be very high. But as a significant number of glaucoma subjects would have an IOP <25 mmHg (remember that close to 50% of glaucomas detected in population are normal-tension glaucomas), the sensitivity (PID – positive in disease) of IOP >25 mmHg in the detection of glaucoma would be low. Suppose we take the IOP cutoff for test positive to be 35 mmHg. Almost no normal subject would have this high an IOP, and the specificity would be very high (>99%); and a highly specific test if positive (for example an IOP >35 mmHg), rules in the disease. Remember this as SpPIN: a highly Specific test if Positive, rules IN disease. Similarly, if we take a cutoff of 12 mmHg, almost no glaucoma subject would have an IOP <12 mmHg (high sensitivity). An eye with an IOP <12 mmHg is extremely unlikely to have glaucoma. A highly sensitive test if negative, rules out the disease. Remember this as SnNOUT: a highly Sensitive test if Negative, rules OUT disease. (Almost all normals would have an IOP >12 mmHg, a very low specificity; but that is a different issue). Another example of SnNOUT would be the absence of venous pulsation in papilledema. The sensitivity of the sign 'absence of venous pulsation' in the diagnosis of papilledema is 99%, and specificity is 90%. So if venous pulsation is present, then we can apply SnNOUT and rule out papilledema. At that point in time, papilledema may be evolving and may still develop a few days or a week later; or patients may have papilledema, but the intracranial pressure at the time of examination is normal.


  Positive Predictive Value (PPV) Top


It is the percentage of patients with a positive test who actually have the disease. In a 2 × 2 table [Table - 1], cell 'a' is 'true positives' and cell 'b' is 'false positives.' In real life situation, we do the new test first and we do not have results of 'gold standard' available. We want to know how this new test is doing. PPV tells us about this – how many of test positives are true positives; and if this number is higher (as close to 100 as possible), then it suggests that this new test is doing as good as 'gold standard.'

PPV: = a / a+b

= a (true positive) / a+b (true positive + false positive)

= Probability (patient having disease when test is positive)

Example: We will use sensitivity and specificity provided in [Table - 3] to calculate positive predictive value.

PPV = a (true positive) / a+b (true positive + false positive)

= 75 / 75 + 15 = 75 / 90 = 83.3%


  Negative Predictive Value (NPV) Top


It is the percentage of patients with a negative test who do not have the disease. In 2 × 2 table [Table - 1], cell 'd' is 'true negatives' and cell 'c' is 'false negatives.' NPV tells us how many of test negatives are true negatives; and if this number is higher (should be close to 100), then it suggests that this new test is doing as good as 'gold standard.'

NPV: = d / c+d

= d (true negative) / c+d (false negative + true negative)

= Probability (patient not having disease when test is negative)

Example: We will use sensitivity and specificity provided in [Table - 3] to calculate negative predictive value.

NPV = a (true negatives) / c+d (false negative + true negative)

= 85 / 85 + 25 = 85 / 110 = 77.3%

Positive and negative predictive values are directly related to the prevalence of the disease in the population [Figure - 1]. Assuming all other factors remain constant, the PPV will increase with increasing prevalence; and NPV decreases with increase in prevalence. This is illustrated by the following example.

A new test has been developed to diagnose primary angle closure glaucoma (PACG). To clarify the terminology used in the example, we will repeat definitions of primary angle closure (PAC) and PACG. PAC is defined as a person with an occludable angle (>180° of posterior trabecular meshwork not visible) with peripheral anterior synechiae with or without raised intraocular pressure (IOP). Optic disc and visual field do not show glaucomatous damage. PACG is defined as PAC with optic disc and visual field changes. PAC affects approximately 3 to 4% of population, while PACG affects approximately 1% of population.

This new test has been performed in 1,000 patients that had documented PACG (disease positive) on gonioscopy (gold standard) and 1,000 normal persons as controls. The authors found that 900 were correctly classified as PACG by the 'new test,' and 950 were correctly labeled as open angle [Table - 4]a. The authors would report the sensitivity and specificity of a test as 90 and 95% respectively. With a sensitivity of 90% and a specificity of 95%, the new test appears to be an excellent test.

Let's apply this test to a million people where only 1% is affected with PACG. Of the million people, 10,000 would be affected with PACG. Since our new test is 90% sensitive, the test will detect 9,000 (TP) people who are actually affected with PACG and miss 1,000 (FN). Looking at those numbers, we would think that our test is very good because we have detected 9,000 out of 10,000 PACG-affected people. However, of the original 1 million, 990,000 are not affected. If we look at the test results on the normal population (remember, the specificity of the test is 95%), we find that while 940,500 are found to be not affected by the new test (TN), we have 49,500 individuals who are found to be positive by the new test (FP).

If we start using this new test without confirmatory testing on the gold standard gonioscopy, we would diagnose 49,500 people, or approximately 5% of the population, as PACG when in reality, they are not. The sensitivity and specificity of the test have not changed. The sensitivity and specificity were however determined with a 50% prevalence of PACG (1,000 PACG and 1,000 normals) with PPV of 95%. We are now applying it to a population with a prevalence of PACG of only 1%. With a 1% prevalence of PACG, the new test has a PPV of 15%. Although the sensitivity and specificity of the test have not changed, the PPV has changed drastically. If the prevalence (also known as the pre-test probability in this situation) of the disease is low, such as with glaucoma or sight-threatening diabetic retinopathy in the general population, the number of false-positive results will be far higher than the number of true-positive results.[3] This leads to a number of problems, including labeling of normal as abnormal resulting in unnecessary treatment.

The NPV of the test also change depending on the prevalence of the disease and usually in reverse direction to PPV. In the above example, in high-prevalence situation (50% prevalence) [Table - 4]a, the NPV was 90%. In low-prevalence situation [Table - 4]b, the NPV increased to 99%. So why not use a test for the NPV value? If the prevalence is already so low, the NPV will certainly reduce it further but still not to zero.

The PPV can increase if we repeat the test in certain situations. For example, in HIV, if we repeat ELISA with different kit in the group that is already ELISA positive, the specificity and PPV will increase. However, if the same test is repeated, then concordance will be a problem.

Everything we have discussed so far has assumed that the sensitivity and specificity do not change as one deals with different groups of people. Sensitivity and specificity, however, can change if the population tested is dramatically different from the population you serve, especially if the spectrum of the disease is different. In more severe disease, we are more likely to be able to make a diagnosis; and thus sensitivity goes up.

What if the new test is actually better than the gold standard? There is no shortcut to the process of comparing it to the existing gold standard. The new (presumably better) test will detect more disease than the 'gold standard.' In the 2 × 2 table, the subjects labeled as 'diseased' by the new test (but still 'normal' on the 'gold standard') will go in cell 'b' (false positives). If on follow-up, a significant number of these patients actually develop disease (gold standard positive), then the new test is in fact detecting disease earlier than, and is better than, the gold standard. In some instances, there may be other strategies available to determine straight away whether the new test is in fact better.[4]


  Clinical application Top


So far we have discussed how to calculate sensitivity, specificity, positive and negative predictive values using 2 × 2 table. Now we will discuss the clinical application of these parameters.

The sensitivity, specificity of IOP, torch light test, van Herick test are shown below [Table - 5].

Which test should we use to screen the population for angle closure glaucoma? The prevalence and PPV discussed above (and other reasons provided in the reference) should have convinced you that this is a bad idea.[3] So let's take an example in a clinic. [Table - 5] shows the sensitivity and specificity of various tests we can use for detecting PACG. Gonioscopy is the 'gold standard' for diagnosis of angle closure, and that's why we should do gonioscopy in all patients we see in clinics. All other tests (IOP, torch light test and van Herick test) have poor specificity.[2],[5],[6] Even with specificity as high as 90%, the PPV will be poor. The prevalence of angle closure (as opposed to angle closure glaucoma) is approximately 3%. With this prevalence, PPV of IOP would be 15%; torch light test, 7.6%; and for van Herick test, 15%. These results mean that if we use IOP or van Herick test to diagnose angle closure, only 15% of suspected angle closure patients will really have disease, and the other 85% would be FP. The sensitivity of these tests is moderate and will miss most of the disease.

In day-to-day clinical practice, we can however combine results of two independent tests to be more confident of the diagnosis – for example, combining IOP and optic disc changes for primary open angle glaucoma (POAG), IOP and peripheral angle chamber depth for diagnosis of PACG, history of diabetes and frequency doubling technology (FDT) defect for diabetic retinopathy.[3]


  Case 1 Top


A 54-year-old male patient was diagnosed to have POAG. He did not have any ocular or systemic complaints. The vision was 20/20, N6 in each eye. The IOPs were 25 mmHg in both eyes on several occasions. Corneal pachymetry was normal and the angle was reported to be open. The optic discs showed changes suggestive of glaucoma, and there were corresponding early visual defects. The patient was started on a unilateral trial of timolol 0.5% twice daily.

The van Herick test when the patient was examined 2 weeks later is shown in [Figure - 2]. The peripheral anterior chamber depth was less than one-fourth the peripheral corneal thickness in both eyes.

With this IOP and van Herick test, a diagnosis of POAG becomes unlikely. Let us examine the rationale behind this statement. The specificity of IOP for glaucoma is 90%. That in itself is not enough for a SpPIN, or 'rule in,' and doesn't help too much. The specificity of the van Herick test for angle closure is 85%, which again, on its own is not of much help either. However, the two tests can be combined to increase the specificity and perhaps apply SpPIN and 'rule in' diagnosis. The specificity of the two tests can be combined in the following manner[6]:

Specificity of combined test = 1 - (1 - specificity of test 1) ´ (1 - specificity of test 2)

Plugging in the values for our patient,

1 - (1 – 0.9) ´ (1 – 0.85) = 1 - 0.1 ´ 0.15 = 1 - 0.015

= 0.985, or 98.5%

This combined specificity of 98.5% definitely allows us to invoke SpPIN and rule in a diagnosis: until proved otherwise, this patient has angle closure. (We assume that the IOP specificity of 90% holds for angle closure glaucoma too.)

The 'open angle' described earlier is shown in [Figure - 3]. The angles on repeat gonioscopy (indentation) are shown in [Figure - 4].

One valid objection to combining tests in this manner is that the resultant sensitivity becomes the product of the sensitivities of the two tests – that is, the product of the sensitivity of an IOP >21 mmHg (50%) and the sensitivity of the van Herick test (69%) = 0.50 ´ 0.69 = 34.5%. While 35 is a low sensitivity as far as tests in general are concerned, it doesn't really matter here as we are utilizing the 'rule in' specifically to make the diagnosis in an individual patient.

Let's take another example: a patient has repeatable IOP measurements of 24 mmHg with normal pachymetry, and the angles this time are really open. The specificity of the IOP measurement is 90%. And, while not too useful a measure, the cup disc ratio is 0.7 (specificity of CDR >0.55 is 73%). The combined specificity of IOP and disc now becomes 1 - (1 - specificity of IOP) ´ (1 - specificity of Disc) = 1 - (1 - 0.90)´(1 - 0.73) = 1- (0.1)´(0.27) = 1 - 0.027 = 97.3%.

This specificity is high enough to "rule in" the diagnosis of POAG, without further testing. Any further testing is probably required for monitoring. Of course, whether we treat or not is a different matter.

Some of us want even more evidence than this. The approach we describe allows incorporation of further testing (including optimal and effective use of modern imaging techniques) too. The GDX 'number (NFI)' in the above patient is more than 32 (specificity of about 85%). If we combine this with just the IOP, can you calculate the combined specificity?

1 - (1 - specificity of IOP)) ´ (1 - specificity of 'number' >30)

You should get 98.5%.

This should be confirmatory; but if you are still not satisfied and want to take it further, you can use the IOP, Disc and the GDX. 1 - (1 - specificity of IOP) ´ (1 - specificity of Disc)´(1 - specificity of 'number' >30).

Did you get 99.5%? As a 'rule in,' this is (almost) as good as it gets. Regrettably, there is no absolute certainty. According to our clinical Bible, absolute certainty is limited to theologians and like-minded clinicians.[1] And as the tests are 'independent,' our estimate of specificity should work. If the tests were not independent, there would be some 'convergence,' as it is technically called. When we use three tests, such convergence would have minimal clinical significance.


  Case 2 Top


A 40-year-old male is suspected to have sarcoidosis. It is an idiopathic multi-system granulomatous disease, where the diagnosis is made by a combination of clinical, radiological and laboratory findings. The gold standard is a tissue biopsy showing noncaseating granuloma. Ocular sarcoidosis could present as anterior, intermediate, posterior or panuveitis; but none of these are pathognomonic. Therefore, one has to rely on ancillary testing to confirm the diagnosis.

Angiotensin-converting enzyme (ACE) has a sensitivity of 73% and a specificity of 83% to diagnose sarcoidosis. Abnormal gallium scan has a sensitivity of 91% and a specificity of 84%.[7] Though individually the specificity of either test is not impressive, when we combine both the tests, the specificity becomes –

1 - (1 - 0.84) × (1 - 0.83) = 1 - (0.16 × 0.17)

= 1 - 0.03 = 0.97 = 97%

The combination sensitivity becomes = 0.73´0.91 = 0.66 = 66%.

Sensitivities can be used in the same manner to rule out diagnoses. Let us assume that the cup disc ratio (usually useless without a mention of the disc size, but having a sensitivity of 50% for a cutoff of >0.55) is 0.6; and the IOP is 21 mmHg (GHT, sensitivity of only 50%). But you feel the disc is suspicious or the patient has a family history or has been referred or whatever. Based on the above information, could the patient still have glaucoma? The combined sensitivity is calculated as:

1 - (1 - sensitivity of IOP)´(1 - sensitivity of CDR >0.55).

Did you try to calculate that? You should get 75%. That's certainly not good enough to rule out a disease like glaucoma. The visual fields, specifically the glaucoma hemifield test (sensitivity 95%), are normal. The combined specificity now becomes 1 - (0.25)´(1 - 0.95) = 98.75. You should be able to rule out 'functional' glaucoma now. Actually a normal field with a normal GHT with a sensitivity of 95% is on its own a good enough 'rule out,' but we know that the field may be normal with a lot of disc damage. So you can use the GDX to combine information about the nerve fiber layer. The 'number' on GDX is 31, the sensitivity of which is 74%. What is the combined sensitivity now? 98.8%. Can we send the patient home now?

In summary, we have provided the basic knowledge to calculate sensitivity, specificity, PPV and NPV. More importantly, we have discussed the advantage and limitations of these measures and provided how we should use these measures in our day-to-day clinical practice. We also have illustrated how to calculate sensitivity and specificity while combining two tests and how to use the results for our patients in day-to-day practice.

 
  References Top

1.
Sackett DL, Haynes RB, Guyatt GH, Tugwell P. Editors. Clinical Epidemiology: A Basic Science for Clinical Medicine. 2nd Edition. New York, Little, Brown and co., 1991, 163-7.  Back to cited text no. 1
    
2.
Van Herick W, Shaffer RN, Schwartz A. Estimation of width of angle of anterior chamber: Incidence and significance of the narrow angle. Am J Ophthalmol 1969;68:626-9.  Back to cited text no. 2
[PUBMED]    
3.
Parikh R, Naik M, Mathai A, Kuriakose T, Muliyil J, Thomas R. Role of frequency doubling technology perimetry in screening of diabetic retinopathy. Indian J Ophthalmol 2006;54:17-22.  Back to cited text no. 3
[PUBMED]  Medknow Journal  
4.
Sackett DL, Haynes RB, Guyatt GH, Tugwell P, editors. Clinical epidemiology: A basic science for clinical medicine. Little, Brown and Co: New York; 1991. p. 51-68.  Back to cited text no. 4
    
5.
Vargas E, Schulzer M, Drance SM. The use of the oblique illumination test to predict angle closure glaucoma. Can J Ophthalmol 1974;9:104-5.  Back to cited text no. 5
[PUBMED]    
6.
Thomas R, George T, Braganza A, Muliyil J. The Flashlight and van Herick's Test are poor predictors of occludable angles. Aust N Z J Ophthalmol 1996;24:251-6.  Back to cited text no. 6
[PUBMED]    
7.
Power WJ, Neves RA, Rodriguez A, Pedroza-Seres M, Foster CS. The value of combined serum angiotensin-converting enzyme and gallium scan in diagnosing ocular sarcoidosis. Ophthalmology 1995;102:2007-11.  Back to cited text no. 7
[PUBMED]    


    Figures

  [Figure - 1], [Figure - 2], [Figure - 3], [Figure - 4]
 
 
    Tables

  [Table - 1], [Table - 2], [Table - 3], [Table - 4], [Table - 5]


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19 Machine learning techniques for chemical and type analysis of ocean oil samples via handheld spectrophotometer device
Katelyn Sosnowski, Andrew Loh, Alanna V. Zubler, Hasina Shir, Sung Yong Ha, Un Hyuk Yim, Jeong-Yeol Yoon
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20 Potential field detection of Flavescence dorée and Esca diseases using a ground sensing optical system
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21 COVID-19 diagnosis from routine blood tests using artificial intelligence techniques
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22 Automatic detection metastasis in breast histopathological images based on ensemble learning and color adjustment
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23 Initial screening of gastric cancer using oral contrast-enhanced trans-abdominal ultrasonography in rural asymptomatic individuals
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24 A hyper-heuristic inspired approach for automatic failure prediction in the context of industry 4.0
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25 Wastewater surveillance demonstrates high predictive value for COVID-19 infection on board repatriation flights to Australia
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26 Modelling fire perimeter formation in the Canadian Rocky Mountains
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27 Evaluation of In-House Cefoxitin Screening Broth to Determine Methicillin-Resistant Staphylococci
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28 Predicting suicide and suicide attempts in adults in acute hospitals: A systematic review of diagnostic accuracy evaluating risk scales
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29 Assessing the effectiveness of a three-way decision-making framework with multiple features in simulating human judgement of opinion classification
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30 Prevalence, determinants and prognostic value of high coronary artery calcium score in asymptomatic patients with diabetes: A systematic review and meta-analysis
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31 Post-mortem blood lead analysis; a comparison between LeadCare II and graphite furnace atomic absorption spectrometry analysis results
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32 Systematic external evaluation of four preoperative risk prediction models for severe postpartum hemorrhage in patients with placenta previa: a multicenter retrospective study
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33 PERFORMANCE EVALUATION FOR CUSTOMER CHURN PREDICTION IN E-COMMERCE USING DEEP LEARNING TECHNIQUE
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34 ABC: Artificial Intelligence for Bladder Cancer grading system
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35 Are newer pharmaceuticals more recalcitrant to removal in wastewater treatment?
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36 Trigger tools to identify adverse drug events in hospitalised children: A systematic review
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37 Pilot study for the development of a screening questionnaire to detect sarcopenic obesity
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38 Modular micro-PCR system for the onsite rapid diagnosis of COVID-19
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39 Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks
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40 Comparison of intuitive assessment and palliative care screening tool in the early identification of patients needing palliative care
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41 AMAISE: a machine learning approach to index-free sequence enrichment
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42 Rapid and specific detection of intact viral particles using functionalized microslit silicon membranes as a fouling-based sensor
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43 Building-level wastewater surveillance using tampon swabs and RT-LAMP for rapid SARS-CoV-2 RNA detection
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46 Commentary: Statistical analysis of 2 x 2 tables in Biomarker studies 1) The Four "indices of test validity"
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47 Abdominal volume index is a better predictor of visceral fat in patients with type 2 diabetes: a cross-sectional study in Ho municipality, Ghana
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48 Audiometric notch as a sign of noise induced hearing loss (NIHL) among the rice and market flour mill workers in Tamil Nadu, South India
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49 Classification of salivary gland tumors in optical coherence tomography images based on deep learning
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51 Diagnostic Accuracy of the Boston Assessment of Traumatic Brain Injury-Lifetime Clinical Interview Compared to Department of Defense Medical Records
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52 Piloting targeted glaucoma screening: experiences of eye care services in Ganjam district, Odisha state, India
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53 Fungal Keratitis: Diagnostic Characteristics of the Potassium Hydroxide Preparation With Calcofluor White in Northern California
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54 Using wearable technology to detect prescription opioid self-administration
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55 Clinical Utility of Semistructured Interview and Scales to Assess Withdrawal Syndromes With Dose Reduction or Discontinuation of Selective Serotonin Reuptake Inhibitors or Serotonin Norepinephrine Reuptake Inhibitors
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56 Assessment of the Wisconsin Criteria at a Level I Trauma Center
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57 Predicting the Travel Distance of Patients to Access Healthcare Using Deep Neural Networks
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58 Trunk-dominant and classic facial pemphigus foliaceus in dogs – comparison of anti-desmocollin-1 and anti-desmoglein-1 autoantibodies and clinical presentations
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59 Linear Regression Equations To Predict ß-Lactam, Macrolide, Lincosamide, and Fluoroquinolone MICs from Molecular Antimicrobial Resistance Determinants in Streptococcus pneumoniae
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60 Diagnostic Efficiency of Determining CXCR1, CXCR2 and Hyaluronic Acid in Blood of Patients with Non-Small Cell Lung Cancer
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61 CNN-LSTM Hybrid Real-Time IoT-Based Cognitive Approaches for ISLR with WebRTC: Auditory Impaired Assistive Technology
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62 The false positive rates for detecting keratoconus and potential ectatic corneal conditions when evaluating astigmatic eyes with Scheimpflug Technology
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63 The Autism-Spectrum Quotient–Hebrew version: Psychometric properties of a full and a short form, adapted for DSM-5
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64 The use and predictive performance of the Peninsula Health Falls Risk Assessment Tool (PH-FRAT) in 25 residential aged care facilities: a retrospective cohort study using routinely collected data
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65 Developing a random forest algorithm to identify patent foramen ovale and atrial septal defects in Ontario administrative databases
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66 Diagnostic ability of a computer algorithm to identify prehospital STEMI
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67 The combined measurement of synovial markers in the diagnosis of periprosthetic joint infection
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68 Population wide testing pooling strategy for SARS-CoV-2 detection using saliva
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69 Yield, NNS and prevalence of screening for DM and hypertension among pulmonary tuberculosis index cases and contacts through single time screening: A contact tracing-based study
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70 Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach
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71 SARS-CoV-2 antibody screening in healthcare workers: lessons learned from the first months of COVID-19 outbreak in Europe. Significance of serology testing for effective pandemic management and reduction of the occupational risk
Daria Burdalska, Adam Konka, Szymon Woroszylo, Kliwia Piórkowska, Joanna Zembala-John, Marlena Golec, Martyna Fronczek, Rafal Jakub Buldak
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72 Successful use of C-MAC® video laryngoscope for unexpected severe airway narrowing during anesthesia induction in a patient with a moderate-sized laryngeal tumor
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73 Circulating cell-free DNA and IL-10 from cerebrospinal fluids aid primary vitreoretinal lymphoma diagnosis
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74 Risk Assessment Instruments for Intimate Partner Femicide: A Systematic Review
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75 Utility of Serum Ki-67 as a Marker for Malignancy in Dogs
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76 The Future of Biomarkers in Veterinary Medicine: Emerging Approaches and Associated Challenges
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77 Application of Deep Learning to Construct Breast Cancer Diagnosis Model
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78 Efficiency of the Adjusted Binary Classification (ABC) Approach in Osteometric Sex Estimation: A Comparative Study of Different Linear Machine Learning Algorithms and Training Sample Sizes
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79 MaasPenn Radiomics Reproducibility Score: A Novel Quantitative Measure for Evaluating the Reproducibility of CT-Based Handcrafted Radiomic Features
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80 Urinary Dipstick Is Not Reliable as a Screening Tool for Albuminuria in the Emergency Department—A Prospective Cohort Study
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81 Preference-Driven Classification Measure
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82 In-Country Method Validation of a Paper-Based, Smartphone-Assisted Iron Sensor for Corn Flour Fortification Programs
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83 Colorimetric Approach for Nucleic Acid Salmonella spp. Detection: A Systematic Review
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84 Administrative Data in Cardiovascular Research—A Comparison of Polish National Health Fund and CRAFT Registry Data
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85 Visual Analytics for Predicting Disease Outcomes Using Laboratory Test Results
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86 Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting
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87 A Deep Ensemble Neural Network with Attention Mechanisms for Lung Abnormality Classification Using Audio Inputs
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88 Correlación diagnóstica de resonancia magnética simple y artroscopia de hombro para inestabilidad de cabeza larga del bíceps como predictor de lesión de subescapular
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89 Validation Study of Algorithms to Identify Malignant Tumors and Serious Infections in a Japanese Administrative Healthcare Database
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90 Comparing pentacam HR screening indices in different normal corneal thicknesses among refractive surgery candidates
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91 Population-based sequencing of Mycobacterium tuberculosis reveals how current population dynamics are shaped by past epidemics
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92 Use of transcranial sonography for the diagnosis of patients with Parkinson's disease and Lewy body dementia
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93 Development of a Diabetic Foot Ulceration Prediction Model and Nomogram
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94 Masked comparison of trypan blue stain and potassium hydroxide with calcofluor white stain in the microscopic examination of corneal scrapings for the diagnosis of microbial keratitis
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95 A New Molecular Detection System for Canine Distemper Virus Based on a Double-Check Strategy
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96 Reverse Transcription Recombinase Polymerase Amplification Assay for Rapid Detection of Avian Influenza Virus H9N2 HA Gene
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97 A Prediction Model for Optimal Primary Debulking Surgery Based on Preoperative Computed Tomography Scans and Clinical Factors in Patients With Advanced Ovarian Cancer: A Multicenter Retrospective Cohort Study
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98 Vascular Auscultation of Carotid Artery: Towards Biometric Identification and Verification of Individuals
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99 VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification
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100 Exploration of Blood Lipoprotein and Lipid Fraction Profiles in Healthy Subjects through Integrated Univariate, Multivariate, and Network Analysis Reveals Association of Lipase Activity and Cholesterol Esterification with Sex and Age
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101 Evaluation of a New Culture-Based AtbFinder Test-System Employing a Novel Nutrient Medium for the Selection of Optimal Antibiotics for Critically Ill Patients with Polymicrobial Infections within 4 h
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102 Evaluation of Available Cognitive Tools Used to Measure Mild Cognitive Decline: A Scoping Review
Chian Thong Chun, Kirsty Seward, Amanda Patterson, Alice Melton, Lesley MacDonald-Wicks
Nutrients. 2021; 13(11): 3974
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103 C-Reactive Protein as a Predictor of Complicated Acute Pancreatitis: Reality or a Myth?
Rami Ahmad, Khalid M Bhatti, Mooyad Ahmed, Kamran Ahmed Malik, Shafiq Rehman, Abdulmoniem Abdulgader, Ambreen Kausar, Ruben Canelo
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104 The impact of false positive COVID-19 results in an area of low prevalence
Brendan Healy, Azizah Khan, Huria Metezai, Ian Blyth, Hibo Asad
Clinical Medicine. 2021; 21(1): e54
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105 Optimizing SARS-CoV-2 Surveillance in the United States: Insights From the National Football League Occupational Health Program
Christina DeFilippo Mack, Michael Osterholm, Erin B. Wasserman, Natalia Petruski-Ivleva, Deverick J. Anderson, Emily Myers, Navdeep Singh, Patti Walton, Gary Solomon, Christopher Hostler, Jimmie Mancell, Allen Sills
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106 Field Performances of Rapid Diagnostic Tests Detecting Human Plasmodium Species: A Systematic Review and Meta-Analysis in India, 1990–2020
Loick Pradel Kojom Foko, Veena Pande, Vineeta Singh
Diagnostics. 2021; 11(4): 590
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107 Brain Hemorrhage Classification in CT Scan Images Using Minimalist Machine Learning
José-Luis Solorio-Ramírez, Magdalena Saldana-Perez, Miltiadis D. Lytras, Marco-Antonio Moreno-Ibarra, Cornelio Yáñez-Márquez
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108 Development of Quantitative Rapid Isothermal Amplification Assay for Leishmania donovani
Md Anik Ashfaq Khan, Khaledul Faisal, Rajashree Chowdhury, Prakash Ghosh, Faria Hossain, Manfred Weidmann, Dinesh Mondal, Ahmed Abd El Wahed
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109 Application of Machine Learning Algorithms to Predict Body Condition Score from Liveweight Records of Mature Romney Ewes
Jimmy Semakula, Rene A. Corner-Thomas, Stephen T. Morris, Hugh T. Blair, Paul R. Kenyon
Agriculture. 2021; 11(2): 162
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110 Determining the Axillary Nodal Status with 4 Current Imaging Modalities, Including 18F-FDG PET/MRI, in Newly Diagnosed Breast Cancer: A Comparative Study Using Histopathology as the Reference Standard
Janna Morawitz, Nils-Martin Bruckmann, Frederic Dietzel, Tim Ullrich, Ann-Kathrin Bittner, Oliver Hoffmann, Svjetlana Mohrmann, Lena Häberle, Marc Ingenwerth, Lale Umutlu, Wolfgang Peter Fendler, Tanja Fehm, Ken Herrmann, Gerald Antoch, Lino Morris Sawicki, Julian Kirchner
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111 Detection of cow hind-leg activity during milking by using a 3-dimensional accelerometer attached to the milking cluster
C.M.C. Raoult, A.A. Margerit, S. Fricker, F.E. Blümel, P.E. Savary
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112 Diagnostic Value of Perfusion-Weighted Magnetic Resonance Imaging as an Adjunct to Routine Magnetic Resonance Protocols for Adults Presenting with Acute Ischemic Stroke
Osama Jaafari, Helen Gallagher, Muhammed Alshehri, Khalid Hakami, Majedh AlShammari
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113 Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review
Howard Maile, Ji-Peng Olivia Li, Daniel Gore, Marcello Leucci, Padraig Mulholland, Scott Hau, Anita Szabo, Ismail Moghul, Konstantinos Balaskas, Kaoru Fujinami, Pirro Hysi, Alice Davidson, Petra Liskova, Alison Hardcastle, Stephen Tuft, Nikolas Pontikos
JMIR Medical Informatics. 2021; 9(12): e27363
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114 Validation of a Musculoskeletal Digital Assessment Routing Tool (DART): Protocol for a Pilot Randomized Crossover Non-Inferiority Trial (Preprint)
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115 Diagnostic efficiency of determining CXCR1, CXCR2 and hyaluronic acid blood level in non-small cell lung cancer patients
D.I. Murashka, A.D. Tahanovich, M.M. Kauhanka, V.I. Prokhorova, O.V. Gotko
Biomeditsinskaya Khimiya. 2021; 67(5): 434
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116 Development and Validation of Multiplex Quantitative PCR Assay for Detection of Helicobacter pylori and Mutations Conferring Resistance to Clarithromycin and Levofloxacin in Gastric Biopsy
Hasyanee Binmaeil, Alfizah Hanafiah, Isa Mohamed Rose, Raja Affendi Raja Ali
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117 Experimental Therapeutic Strategies in Epilepsies Using Anti-Seizure Medications
Fakher Rahim, Reza Azizimalamiri, Mehdi Sayyah, Alireza Malayeri
Journal of Experimental Pharmacology. 2021; Volume 13: 265
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118 Validating International Classification of Disease 10th Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations
Mackenzie A. Hamilton, Andrew Calzavara, Scott D. Emerson, Mohamed Djebli, Maria E. Sundaram, Adrienne K. Chan, Rafal Kustra, Stefan D. Baral, Sharmistha Mishra, Jeffrey C. Kwong, Judith Katzenellenbogen
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119 Automatic methods of hoof-on and -off detection in horses using wearable inertial sensors during walk and trot on asphalt, sand and grass
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120 Assessment of cognitive screening tests as predictors of driving cessation: A prospective cohort study of a median 4-year follow-up
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121 Immunological detection of human and camel cystic echinococcosis using different antigens of hydatid cyst fluid, protoscoleces, and germinal layers
Mohey A. Hassanain, Nagwa I. Toaleb, Raafat M. Shaapan, Nawal A. Hassanain, Ahmed Maher, Ahmed B. Yousif
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122 Evaluation of binary diagnostic tests accuracy for medical researches
Jale Karakaya
Turkish Journal of Biochemistry. 2021; 46(2): 103
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123 Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology
Adrian J. Green, Martin J. Mohlenkamp, Jhuma Das, Meenal Chaudhari, Lisa Truong, Robyn L. Tanguay, David M. Reif, Vassily Hatzimanikatis
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124 Identification of high risk and early stage eating disorders: first validation of a digital screening tool
Emma Bryant, Jane Miskovic-Wheatley, Stephen W. Touyz, Ross D. Crosby, Eyza Koreshe, Sarah Maguire
Journal of Eating Disorders. 2021; 9(1)
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125 Comparative study of immunohematological tests with canine blood samples submitted for a direct antiglobulin (Coombs’) test
Nadine Idalan, Johanna O. Zeitz, Corinna N. Weber, Elisabeth Müller, Urs Giger
Canine Medicine and Genetics. 2021; 8(1)
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126 Predictors of False Negative Sentinel Lymph Node Biopsy in Clinically Localized Merkel Cell Carcinoma
Richard J. Straker, Michael J. Carr, Andrew J. Sinnamon, Adrienne B. Shannon, James Sun, Karenia Landa, Kirsten M. Baecher, Christian Wood, Kevin Lynch, Harrison G. Bartels, Robyn Panchaud, Michael C. Lowe, Craig L. Slingluff, Mark J. Jameson, Kenneth Tsai, Mark B. Faries, Georgia M. Beasley, Vernon Sondak, Giorgos C. Karakousis, Jonathan S. Zager, John T. Miura
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127 Measuring mental health in humanitarian crises: a practitioner’s guide to validity
Brandon A. Kohrt, Bonnie N. Kaiser
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128 Biogeographical characteristics of Schistosoma mansoni endemic areas in Ethiopia: a systematic review and meta analysis
Keerati Ponpetch, Berhanu Erko, Teshome Bekana, Lindsay Richards, Song Liang
Infectious Diseases of Poverty. 2021; 10(1)
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129 Prevalence, antimicrobial susceptibility pattern and associated risk factors for urinary tract infections in pregnant women attending ANC in some integrated health centers in the Buea Health District
Innocentia Nji Ngong, Jerome Fru-Cho, Melduine Akom Yung, Jane-Francis Kihla Tatah Akoachere
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130 Validation of the intolerance of uncertainty scale as a screening tool for perinatal anxiety
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BMC Pregnancy and Childbirth. 2021; 21(1)
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131 The diagnostic threshold of Cornell assessment of pediatric delirium in detection of postoperative delirium in pediatric surgical patients
Hong Hong, Chao Guo, Zhi-Hua Liu, Bo-Jie Wang, Shu-Zhe Zhou, Dong-Liang Mu, Dong-Xin Wang
BMC Pediatrics. 2021; 21(1)
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132 ACTION trial: a prospective study on diagnostic Accuracy of 4D CT for diagnosing Instable ScaphOlunate DissociatioN
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BMC Musculoskeletal Disorders. 2021; 22(1)
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133 Beijing Friendship Hospital Osteoporosis Self-Assessment Tool for Elderly Male (BFH-OSTM) vs Fracture Risk Assessment Tool (FRAX) for identifying painful new osteoporotic vertebral fractures in older Chinese men: a cross-sectional study
Ning An, Ji Sheng Lin, Qi Fei
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134 Contextual property detection in Dutch diagnosis descriptions for uncertainty, laterality and temporality
Eva S. Klappe, Florentien J. P. van Putten, Nicolette F. de Keizer, Ronald Cornet
BMC Medical Informatics and Decision Making. 2021; 21(1)
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135 Measurement of Tryptase and CC16/Albumin in Nasal Lavage Fluid as a Screening Tool of Allergic Rhinitis
Oh Eun Kwon, Young Chan Lee, Jung Min Park, Sung Wan Kim, Young-Gyu Eun, Seong-Gyu Ko
American Journal of Rhinology & Allergy. 2021; 35(6): 768
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136 Failure to Achieve Threshold Scores on Patient-Reported Outcome Measures Within 1 Year Has a Predictive Risk of Subsequent Hip Surgery Within 5 Years of Primary Hip Arthroscopy: A Case-Control Study
Jacob D. Feingold, Erica L. Swartwout, Sacha A. Roberts, Benedict U. Nwachukwu, Anil S. Ranawat
Orthopaedic Journal of Sports Medicine. 2021; 9(11): 2325967121
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137 Vision Evaluation Tools for Adults With Acquired Brain Injury: A Scoping Review
Camille Dubé, Yu Jin, Brienne G. Powers, Ginny Li, Amélie Labelle, Meghan S. Rivers, Ivy M. Gumboc, André E. Bussières
Canadian Journal of Occupational Therapy. 2021; : 0008417421
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138 The Utility of Cognitive Screeners in the Detection of Dementia Spectrum Disorders in Spanish-Speaking Populations
Shanna L. Burke, Adrienne Grudzien, Aaron Burgess, Miriam J. Rodriguez, Yesenia Rivera, David Loewenstein
Journal of Geriatric Psychiatry and Neurology. 2021; 34(2): 102
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139 Practical and validated tool to assess falls risk in the primary care setting: a systematic review
Wytske MA Meekes, Joke C Korevaar, Chantal J Leemrijse, Ien AM van de Goor
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140 Large-scale study on virological and serological prevalence of SARS-CoV-2 in cats and dogs in Spain
Sandra Barroso-Arévalo, Alberto Barneto, Ángel Manuel Ramos, Belén Rivera, Rocío Sánchez, Lidia Sánchez-Morales, Marta Pérez-Sancho, Aránzazu Buendía, Elisa Ferreras, Juan Carlos Ortiz-Menéndez, Inmaculada Moreno, Consuelo Serres, Carmen Vela, María Ángeles Risalde, Lucas Domínguez, José M. Sánchez-Vizcaíno
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141 Evaluation of immunodiagnostic tests for human gnathostomiasis using different antigen preparations of Gnathostoma spinigerum larvae against IgE, IgM, IgG, IgG1-4 and IgG1 patterns of post-treated patients
Issariya Ieamsuwan, Dorn Watthanakulpanich, Urai Chaisri, Poom Adisakwattana, Paron Dekumyoy
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142 Transfer Learning and Semisupervised Adversarial Detection and Classification of COVID-19 in CT Images
Ariyo Oluwasanmi, Muhammad Umar Aftab, Zhiguang Qin, Son Tung Ngo, Thang Van Doan, Son Ba Nguyen, Son Hoang Nguyen, Dan Selisteanu
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143 Postanesthetic Cold Sensibility Test as an Indicator for the Efficacy of Inferior Alveolar Nerve Block in Patients with Symptomatic Irreversible Pulpitis of Mandibular Molars
Mohamed El Sayed, Kamis Gaballah, Murilo Baena Lopes
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144 Drug Disease Relation Extraction from Biomedical Literature Using NLP and Machine Learning
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145 Detection of pathogens in blood or feces of adult horses with enteric disease and association with outcome of colitis
Jamie J. Kopper, Jaclyn A. Willette, Clark J. Kogan, Alexis Seguin, Steven R. Bolin, Harold C. Schott
Journal of Veterinary Internal Medicine. 2021; 35(5): 2465
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146 Analyzing Risk of Service Failures in Heavy Haul Rail Lines: A Hybrid Approach for Imbalanced Data
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147 Improving the efficiency of the autoverification workflow for nucleated red blood cell reporting in the hematology laboratory
Pawadee Chinudomwong, Narin Khongjaroensakun, Benjarat Chatachote, Nutdanai Chaothai, Karan Paisooksantivatana
International Journal of Laboratory Hematology. 2021; 43(6): 1373
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148 A comparison of computed tomography, X-ray and Lodox ® scans in assessing pediatric skull fractures using piglets
Amy Joy Spies, Maryna Steyn, Desiré Brits
Journal of Forensic Sciences. 2021; 66(2): 470
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149 Establishing cut-off values for mild, moderate and severe disease in patients with pemphigus using the Pemphigus Disease Area Index
R.L. Krain, C.E. Bax, S. Chakka, S. Ahmed, R. Feng, A.S. Payne, V.P. Werth
British Journal of Dermatology. 2021; 184(5): 975
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150 Detection of anomalies in the red reflex test requires adequate training
Lindsey Rose, John Siderov, Hanita Bhopal, Sheila Mok
Clinical and Experimental Optometry. 2021; 104(1): 95
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151 Discrimination of Diabetic Retinopathy From Optical Coherence Tomography Angiography Images Using Machine Learning Methods
Zhiping Liu, Chen Wang, Xiaodong Cai, Hong Jiang, Jianhua Wang
IEEE Access. 2021; 9: 51689
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152 Detecting Progression in Patients With Different Clinical Presentations of Primary Open-angle Glaucoma
Sampson L. Abu, Iván Marín-Franch, Lyne Racette
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153 Asymmetric multiscale multifractal analysis (AMMA) of heart rate variability
Dorota Kokosinska, Jan Jacek Zebrowski, Teodor Buchner, Rafal Baranowski, Ewa Orlowska-Baranowska
Physiological Measurement. 2021; 42(8): 085003
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154 Validation of human microRNA target pathways enables evaluation of target prediction tools
Fabian Kern, Lena Krammes, Karin Danz, Caroline Diener, Tim Kehl, Oliver Küchler, Tobias Fehlmann, Mustafa Kahraman, Stefanie Rheinheimer, Ernesto Aparicio-Puerta, Sylvia Wagner, Nicole Ludwig, Christina Backes, Hans-Peter Lenhof, Hagen von Briesen, Martin Hart, Andreas Keller, Eckart Meese
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155 Accuracy of an algorithm to identify rheumatoid arthritis in the Longitudinal Ageing Study Amsterdam population: a validation study
MM ter Wee, HG Raterman, NM van Schoor, DJH Deeg, WF Lems, MT Nurmohamed, S Simsek
Scandinavian Journal of Rheumatology. 2021; 50(4): 290
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156 Myelin-associated proteins are potential diagnostic markers in patients with primary brain tumour
Olga M. Koper-Lenkiewicz, Anna J. Milewska, Joanna Kaminska, Karol Sawicki, Robert Chrzanowski, Justyna Zinczuk, Joanna Reszec, Marzena Tylicka, Ewa Matuszczak, Joanna Matowicka-Karna, Zenon Mariak, Mariusz W. Mucha, Robert Pawlak, Violetta Dymicka-Piekarska
Annals of Medicine. 2021; 53(1): 1710
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157 Organic dots (O-dots) for theranostic applications: preparation and surface engineering
Amin Shiralizadeh Dezfuli, Elmira Kohan, Sepand Tehrani Fateh, Neda Alimirzaei, Hamidreza Arzaghi, Michael R. Hamblin
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158 Association of acute and chronic workloads with injury risk in high-performance junior tennis players
Victor Moreno-Pérez, Jaime Prieto, Juan Del Coso, José Ezequiel Lidó-Micó, Miguel Fragoso, Francisco José Penalva, Machar Reid, Babette M. Pluim
European Journal of Sport Science. 2021; 21(8): 1215
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159 Data augmentation using image-to-image translation for detecting forest strip roads based on deep learning
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International Journal of Forest Engineering. 2021; 32(1): 57
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160 The Impact of Pass/Refer Criteria in the Use of Otoacoustic Emission Technology for Newborn Hearing Screening
Amisha Kanji, Alida Naudé
American Journal of Audiology. 2021; 30(2): 416
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161 Machine learning methods to predict amyloid positivity using domain scores from cognitive tests
Guogen Shan, Charles Bernick, Jessica Z. K. Caldwell, Aaron Ritter
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162 Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning
Kelly M. Makielski, Alicia J. Donnelly, Ali Khammanivong, Milcah C. Scott, Andrea R. Ortiz, Dana C. Galvan, Hirotaka Tomiyasu, Clarissa Amaya, Kristin A. Ward, Alexa Montoya, John R. Garbe, Lauren J. Mills, Gary R. Cutter, Joelle M. Fenger, William C. Kisseberth, Timothy D. O’Brien, Brenda J. Weigel, Logan G. Spector, Brad A. Bryan, Subbaya Subramanian, Jaime F. Modiano
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163 Nanodiagnostics to Face SARS-CoV-2 and Future Pandemics: From an Idea to the Market and Beyond
Giulio Rosati, Andrea Idili, Claudio Parolo, Celia Fuentes-Chust, Enric Calucho, Liming Hu, Cecilia de Carvalho Castro e Silva, Lourdes Rivas, Emily P. Nguyen, José F. Bergua, Ruslan Alvárez-Diduk, José Muñoz, Christophe Junot, Oriol Penon, Dominique Monferrer, Emmanuel Delamarche, Arben Merkoçi
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164 Aggregation of Abnormal Memory Scores and Risk of Incident Alzheimer’s Disease Dementia: A Measure of Objective Memory Impairment in Amnestic Mild Cognitive Impairment
Nicholas I. Bradfield, Kathryn A. Ellis, Greg Savage, Paul Maruff, Samantha Burnham, David Darby, Nicola T. Lautenschlager, Ralph N. Martins, Colin L. Masters, Stephanie R. Rainey-Smith, Joanne Robertson, Christopher Rowe, Michael Woodward, David Ames
Journal of the International Neuropsychological Society. 2021; 27(2): 146
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165 Brief, Performance-Based Cognitive Screening in Youth Aged 12–25: A Systematic Review
Shayden D. Bryce, Stephen C. Bowden, Stephen J. Wood, Kelly Allott
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166 Peptide-Spectrum Match Validation with Internal Standards (P-VIS): Internally-Controlled Validation of Mass Spectrometry-Based Peptide Identifications
Timothy Aaron Wiles, Laura M. Saba, Thomas Delong
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167 Analysis of Metabolite and Lipid Association Networks Reveals Molecular Mechanisms Associated with 3-Month Mortality and Poor Functional Outcomes in Patients with Acute Ischemic Stroke after Thrombolytic Treatment with Recombinant Tissue Plasminogen Activ
Cristina Licari, Leonardo Tenori, Betti Giusti, Elena Sticchi, Ada Kura, Rosina De Cario, Domenico Inzitari, Benedetta Piccardi, Mascia Nesi, Cristina Sarti, Francesco Arba, Vanessa Palumbo, Patrizia Nencini, Rossella Marcucci, Anna Maria Gori, Claudio Luchinat, Edoardo Saccenti
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168 The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review
Wessel T. Stam, Lotte K. Goedknegt, Erik W. Ingwersen, Linda J. Schoonmade, Emma R.J. Bruns, Freek Daams
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169 Technical considerations to development of serological tests for SARS-CoV-2
Emilie Ernst, Patricia Wolfe, Corrine Stahura, Katie A. Edwards
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170 Diagnostic accuracy of the short-form Fonseca Anamnestic Index in relation to the Diagnostic Criteria for Temporomandibular Disorders
Adrian Ujin Yap, Min-Juan Zhang, Jie Lei, Kai-Yuan Fu
The Journal of Prosthetic Dentistry. 2021;
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171 Autofluorescence spectroscopy and multivariate analysis for predicting the induced damages to other organs due to liver fibrosis
Shaiju S. Nazeer, T.P. Sreedevi, Ramapurath S. Jayasree
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2021; 257: 119741
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172 Age-gender specific prediction model for Parkinson’s severity assessment using gait biomarkers
Preeti Khera, Neelesh Kumar
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173 Obesity and visceral fat in Indonesia: An unseen epidemic? A study using iDXA and surrogate anthropometric measures
Marc K. Smith, Erwin Christianto, Jonathan M.D. Staynor
Obesity Research & Clinical Practice. 2021; 15(1): 26
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174 Effectiveness of magnetic resonance imaging–targeted biopsy for detection of prostate cancer in comparison with systematic biopsy in our countries with low prevalence of prostate cancer: our first experience after 3 years
Mostafa A. Arafa, Danny M. Rabah, Farruhk K. Khan, Karim H. Farhat, Mohamed A. Al-Atawi
Prostate International. 2021; 9(3): 140
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175 Comparison of hemagglutination inhibition tests, immunoperoxidase monolayer assays, and serum neutralizing tests in detecting antibodies against blue eye disease in pigs
Diego Rafael Hidalgo-Lara, Jazmín De la Luz-Armendáriz, José Francisco Rivera-Benítez, Luis Gomez-Nuñez, Erika Nayeli Salazar-Jiménez, Tania Lucia Madrigal-Valencia, Humberto Ramírez-Mendoza
Journal of Immunological Methods. 2021; 496: 113088
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176 Mechanical symptoms and meniscal tear: a reappraisal
C.G. McHugh, E.G. Matzkin, J.N. Katz
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177 Performance of five rapid serological tests in mild-diseased subjects using finger prick blood for exposure assessment to SARS-CoV-2
David Triest, Laurence Geebelen, Robby De Pauw, Stéphane De Craeye, Alexandra Vodolazkaia, Mathieu Verbrugghe, Koen Magerman, Lara-Lauren Robben, Pieter Pannus, Kristof Neven, Dirk Ramaekers, Steven Van Gucht, Katelijne Dierick, Nele Van Loon, Maria E. Goossens, Isabelle Desombere
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178 Brain Signatures During Reward Anticipation Predict Persistent Attention-Deficit/Hyperactivity Disorder Symptoms
Di Chen, Tianye Jia, Wei Cheng, Miao Cao, Tobias Banaschewski, Gareth J. Barker, Arun L.W. Bokde, Uli Bromberg, Christian Büchel, Sylvane Desrivières, Herta Flor, Antoine Grigis, Hugh Garavan, Penny A. Gowland, Andreas Heinz, Bernd Ittermann, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Frauke Nees, Dimitri Papadopoulos Orfanos, Tomáš Paus, Luise Poustka, Juliane H. Fröhner, Michael N. Smolka, Henrik Walter, Robert Whelan, T.W. Robbins, Barbara J. Sahakian, Gunter Schumann, Jianfeng Feng
Journal of the American Academy of Child & Adolescent Psychiatry. 2021;
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179 Acute Appendicitis in Pediatric Patients With Sickle Cell Disease: Lower Incidence, More Imaging, and More False-Positives
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Journal of the American College of Radiology. 2021; 18(2): 257
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180 Concurrent validity of the Brazilian Portuguese version of the Questionnaire on Eating and Weight Patterns-5 (QEWP-5) in the general population
Carlos Eduardo Ferreira de Moraes, Carla Mourilhe, Glória Valéria da Veiga, Sílvia Regina de Freitas, Ronir Raggio Luiz, Phillipa Hay, Jose Carlos Appolinario
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181 Pruning of generative adversarial neural networks for medical imaging diagnostics with evolution strategy
Francisco Erivaldo Fernandes, Gary G. Yen
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182 Using i-vectors from voice features to identify major depressive disorder
Yazheng Di, Jingying Wang, Weidong Li, Tingshao Zhu
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183 Reliability and Concurrent Validity of the SARC-F and Its Modified Versions: A Systematic Review and Meta-Analysis
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Journal of the American Medical Directors Association. 2021; 22(9): 1864
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184 Earthquake vulnerability assessment for the Indian subcontinent using the Long Short-Term Memory model (LSTM)
Ratiranjan Jena, Sambit Prasanajit Naik, Biswajeet Pradhan, Ghassan Beydoun, Hyuck-Jin Park, Abdullah Alamri
International Journal of Disaster Risk Reduction. 2021; 66: 102642
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185 Significant symptoms and nonsymptom-related factors for malaria diagnosis in endemic regions of Indonesia
Yulianti Paula Bria, Chung-Hsing Yeh, Susan Bedingfield
International Journal of Infectious Diseases. 2021; 103: 194
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186 Can forensic anthropologists accurately detect skeletal trauma using radiological imaging?
Amy Joy Spies, Maryna Steyn, Daniel Nicholas Prince, Desiré Brits
Forensic Imaging. 2021; 24: 200424
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187 GAD-7, GAD-2, and GAD-mini: Psychometric properties and norms of university students in the United States
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General Hospital Psychiatry. 2021; 69: 61
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188 Near-focus narrow-band imaging classification of villous atrophy in suspected celiac disease: development and international validation
Shraddha Gulati, Andrew Emmanuel, Mark Ong, Polychronis Pavlidis, Mehul Patel, Tareq El-Menabawey, Zuzana Vackova, Patrick Dubois, Alberto Murino, Jan Martinek, Amrita Sethi, Helmut Neumann, Amyn Haji, Bu’Hussain Hayee
Gastrointestinal Endoscopy. 2021; 94(6): 1071
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189 On the estimation of sugars concentrations using Raman spectroscopy and artificial neural networks
N. González-Viveros, P. Gómez-Gil, J. Castro-Ramos, H.H. Cerecedo-Núñez
Food Chemistry. 2021; 352: 129375
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190 Evaluation of diagnostic accuracy of loop-mediated isothermal amplification method (LAMP) compared with polymerase chain reaction (PCR) for Leptospira spp. in clinical samples: a systematic review and meta-analysis
Shan Gunasegar, Vasantha Kumari Neela
Diagnostic Microbiology and Infectious Disease. 2021; 100(3): 115369
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191 Evolutionary algorithm-based convolutional neural network for predicting heart diseases
Ali A. Samir, Abdullah R. Rashwan, Karam M. Sallam, Ripon K. Chakrabortty, Michael J. Ryan, Amr A. Abohany
Computers & Industrial Engineering. 2021; 161: 107651
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192 A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data
Erick Martinez-Ríos, Luis Montesinos, Mariel Alfaro-Ponce, Leandro Pecchia
Biomedical Signal Processing and Control. 2021; 68: 102813
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193 Using interpretability approaches to update “black-box” clinical prediction models: an external validation study in nephrology
Harry Freitas da Cruz, Boris Pfahringer, Tom Martensen, Frederic Schneider, Alexander Meyer, Erwin Böttinger, Matthieu-P. Schapranow
Artificial Intelligence in Medicine. 2021; 111: 101982
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194 Low back pain expert systems: Clinical resolution through probabilistic considerations and poset
Debarpita Santra, Subrata Goswami, Jyotsna Kumar Mandal, Swapan Kumar Basu
Artificial Intelligence in Medicine. 2021; 120: 102163
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195 Predicting length of stay in hospitals intensive care unit using general admission features
Merhan A. Abd-Elrazek, Ahmed A. Eltahawi, Mohamed H. Abd Elaziz, Mohamed N. Abd-Elwhab
Ain Shams Engineering Journal. 2021; 12(4): 3691
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196 A review of biosensor technologies for blood biomarkers toward monitoring cardiovascular diseases at the point-of-care
Mengxing Ouyang, Dandan Tu, Lin Tong, Mehenur Sarwar, Arvind Bhimaraj, Chenzhong Li, Gerard L. Coté, Dino Di Carlo
Biosensors and Bioelectronics. 2021; 171: 112621
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197 Development and psychometric evaluation of a Thai Diagnostic Autism Scale for the early diagnosis of Autism Spectrum Disorder
Duangkamol Tangviriyapaiboon, Samai Sirithongthaworn, Hataichanok Apikomonkon, Chidawan Suyakong, Pimwarat Srikummoon, Suttipong Kawilapat, Patrinee Traisathit
Autism Research. 2021;
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198 New feature selection paradigm based on hyper-heuristic technique
Rehab Ali Ibrahim, Mohamed Abd Elaziz, Ahmed A. Ewees, Mohammed El-Abd, Songfeng Lu
Applied Mathematical Modelling. 2021; 98: 14
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199 Correlation of anal cytology with follow-up histology and Human Papillomavirus genotyping: A 10-year experience from an academic medical center
Amanda M. Hopp, Mamta Pant, Sally Sniedze, Lauren N. Parsons, Bryan Hunt, Tamara Giorgadze
Annals of Diagnostic Pathology. 2021; 50: 151670
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200 Sensitivity and specificity of the driver sleepiness detection methods using physiological signals: A systematic review
Christopher N. Watling, Md Mahmudul Hasan, Grégoire S. Larue
Accident Analysis & Prevention. 2021; 150: 105900
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201 Assessing Neuropsychological Functions in Middle Childhood: a Narrative Review of Measures and Their Psychometric Properties Across Context
Maina Rachel, Van De Vijver J. R. Fons, Abubakar Amina, Miguel Perez-Garcia, Kumar Manasi
Journal of Pediatric Neuropsychology. 2021; 7(3): 113
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202 On the goodness of fit of parametric and non-parametric data mining techniques: the case of malaria incidence thresholds in Uganda
Francis Fuller Bbosa, Josephine Nabukenya, Peter Nabende, Ronald Wesonga
Health and Technology. 2021; 11(4): 929
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203 A comparative study and analysis of LSTM deep neural networks for heartbeats classification
Srinidhi Hiriyannaiah, Siddesh G M, Kiran M H M, K G Srinivasa
Health and Technology. 2021; 11(3): 663
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204 Low-dose cone-beam computed tomography in simulated condylar erosion detection: a diagnostic accuracy study
Noha Saleh Abu-Taleb, Dina Mohamed ElBeshlawy
Oral Radiology. 2021; 37(3): 427
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205 Interexaminer reliability for tomographic findings in temporomandibular joint degenerative disease and its agreement with clinical diagnosis: a blinded controlled cross sectional study
Priscila Brenner Hilgenberg-Sydney, Luís Felipe Schenato, Helena Bussular Marques, Fernanda Mara de Paiva Bertoli, Daniel Bonotto
Oral Radiology. 2021;
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206 Detection of Porcine Cysticercosis in Meat Juice Samples from Infected Pigs
Justine Daudi Maganira, Winifrida Kidima, Chacha John Mwita, Johan Höglund
Acta Parasitologica. 2021; 66(3): 851
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207 Serial use of existing clinical decisions aids can reduce computed tomography pulmonary angiography for pulmonary embolism
Robert Russell Ehrman, Adrienne Nicole Malik, Reid Kenneth Smith, Zeid Kalarikkal, Andrew Huang, Ryan Michael King, Rubin David Green, Brian James O’Neil, Robert Leigh Sherwin
Internal and Emergency Medicine. 2021; 16(8): 2251
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208 The Predictive Value of the AQ and the SRS-A in the Diagnosis of ASD in Adults in Clinical Practice
M. L. Bezemer, E. M. A. Blijd-Hoogewys, M. Meek-Heekelaar
Journal of Autism and Developmental Disorders. 2021; 51(7): 2402
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209 Phenology based classification index method for land cover mapping from hyperspectral imagery
KR. Sivabalan, E. Ramaraj
Multimedia Tools and Applications. 2021; 80(9): 14321
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210 Virtual-Reality Performance-Based Assessment of Cognitive Functions in Adult Patients With Acquired Brain Injury: A Scoping Review
Claudia Corti, Maria Chiara Oprandi, Mathilde Chevignard, Ashok Jansari, Viola Oldrati, Elisabetta Ferrari, Monica Martignoni, Romina Romaniello, Sandra Strazzer, Alessandra Bardoni
Neuropsychology Review. 2021;
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211 Trends and Challenges of Processing Measurements from Wearable Devices Intended for Epileptic Seizure Prediction
Yankun Xu, Jie Yang, Mohamad Sawan
Journal of Signal Processing Systems. 2021;
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212 Deep learning based detection and analysis of COVID-19 on chest X-ray images
Rachna Jain, Meenu Gupta, Soham Taneja, D. Jude Hemanth
Applied Intelligence. 2021; 51(3): 1690
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213 Low accuracy of self-reported family history of melanoma in high-risk patients
Nicholas D. Flint, Michael D. Bishop, Tristan C. Smart, Jennifer L. Strunck, Kenneth M. Boucher, Douglas Grossman, Aaron M. Secrest
Familial Cancer. 2021; 20(1): 41
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214 Sensitivity and specificity of MultiColor imaging in detecting proliferative diabetic retinopathy
Sara Vaz-Pereira, Tiago Morais-Sarmento, Gabriella De Salvo
International Ophthalmology. 2021;
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215 Evaluating the accuracy of facial expressions as emotion indicators across contexts in dogs
A. Bremhorst, D. S. Mills, H. Würbel, S. Riemer
Animal Cognition. 2021;
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216 Diagnostic accuracy of AS-OCT vs gonioscopy for detecting angle closure: a systematic review and meta-analysis
Thomas Desmond, Vincent Tran, Monish Maharaj, Nicole Carnt, Andrew White
Graefe's Archive for Clinical and Experimental Ophthalmology. 2021;
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217 Correlation of procalcitonin to positive blood culture results in a sample of South African trauma ICU patients between 2016 and 2017
Dirouvarlen Ramasawmy, Maheshan Pillay, Timothy Craig Hardcastle
European Journal of Trauma and Emergency Surgery. 2021; 47(4): 1183
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218 Deep learning–based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals
Jong Hyuk Lee, Sunggyun Park, Eui Jin Hwang, Jin Mo Goo, Woo Young Lee, Sangho Lee, Hyungjin Kim, Jason R. Andrews, Chang Min Park
European Radiology. 2021; 31(2): 1069
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219 Wearable activity trackers and artificial intelligence in the management of rheumatic diseases
Thomas Davergne, Joanna Kedra, Laure Gossec
Zeitschrift für Rheumatologie. 2021;
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220 Diagnostic accuracy of the Clinical Dementia Rating Scale for detecting mild cognitive impairment and dementia: A bivariate meta-analysis
Hui-Chuan Huang, Yu-Min Tseng, Yi-Chun Chen, Pin-Yuan Chen, Hsiao-Yean Chiu
International Journal of Geriatric Psychiatry. 2021; 36(2): 239
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221 Gastric Residual to Predict Necrotizing Enterocolitis in Preterm Piglets As Models for Infants
Susanne Soendergaard Kappel, Per Torp Sangild, Linda Hilsted, Bolette Hartmann, Thomas Thymann, Lise Aunsholt
Journal of Parenteral and Enteral Nutrition. 2021; 45(1): 87
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222 Comparison of risk-scoring systems for heparin-induced thrombocytopenia in cardiac surgery patients
Jackson J. Stewart, Ricky Turgeon, Arabesque Parker, Sheri Koshman, Mohamed A. Omar
Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy. 2021;
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223 Examining the predictive validity of behavior screeners across measures and respondents
Corey Jones, Emily Graybill, Brian Barger, Andrew T. Roach
Psychology in the Schools. 2020; 57(6): 923
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224 Prediction of lithium treatment response in bipolar depression using 5-HTT and 5-HT1A PET
Mala Ananth, Elizabeth A. Bartlett, Christine DeLorenzo, Xuejing Lin, Laura Kunkel, Nehal P. Vadhan, Greg Perlman, Michala Godstrey, Daniel Holzmacher, R. Todd Ogden, Ramin V. Parsey, Chuan Huang
European Journal of Nuclear Medicine and Molecular Imaging. 2020; 47(10): 2417
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225 Qualitative threshold method validation and uncertainty evaluation: A theoretical framework and application to a 40 analytes liquid chromatography–tandem mass spectrometry method
Félix Camirand Lemyre, Brigitte Desharnais, Julie Laquerre, Marc-André Morel, Cynthia Côté, Pascal Mireault, Cameron D. Skinner
Drug Testing and Analysis. 2020; 12(9): 1287
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226 Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents
Joy T. Wu, Ken C. L. Wong, Yaniv Gur, Nadeem Ansari, Alexandros Karargyris, Arjun Sharma, Michael Morris, Babak Saboury, Hassan Ahmad, Orest Boyko, Ali Syed, Ashutosh Jadhav, Hongzhi Wang, Anup Pillai, Satyananda Kashyap, Mehdi Moradi, Tanveer Syeda-Mahmood
JAMA Network Open. 2020; 3(10): e2022779
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227 Gender Differences in the Utility of the Alcohol Use Disorder Identification Test in Screening for Alcohol Use Disorder Among HIV Test Seekers in South Africa
W. Saal, A. Kagee, J. Bantjes
AIDS and Behavior. 2020; 24(7): 2073
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228 Reliability and Validity of the M-MALMAS Instrument to Assess Medication Adherence in Malay-Speaking Patients with Type 2 Diabetes
Pauline Siew Mei Lai, Renukha Sellappans, Siew Siang Chua
Pharmaceutical Medicine. 2020; 34(3): 201
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229 Improved ballistic limit equations for high-speed non-aluminum projectiles impacting aluminum dual-wall spacecraft systems
William P. Schonberg
SN Applied Sciences. 2020; 2(8)
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230 Development and validation of gaming disorder and hazardous gaming scale (GDHGS) based on the WHO framework (ICD-11 criteria) of disordered gaming
Yatan Pal Singh Balhara, Swarndeep Singh, Romil Saini, Dheeraj Kattula, Surekha Chukkali, Rachna Bhargava
Asian Journal of Psychiatry. 2020; 54: 102348
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231 A meta-analysis of the accuracy of a neuroendocrine tumor mRNA genomic biomarker (NETest) in blood
K. Öberg, A. Califano, J.R. Strosberg, S. Ma, U. Pape, L. Bodei, G. Kaltsas, C. Toumpanakis, J.R. Goldenring, A. Frilling, S. Paulson
Annals of Oncology. 2020; 31(2): 202
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232 Hyperspectral imaging for identification of Zebra Chip disease in potatoes
Abhimanyu Singh Garhwal, Reddy R. Pullanagari, Mo Li, Marlon M. Reis, Richard Archer
Biosystems Engineering. 2020; 197: 306
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233 Inter-rater reliability of amplitude-integrated EEG for the detection of neonatal seizures
Abhijeet A. Rakshasbhuvankar, Deepika Wagh, Sam E. Athikarisamy, Jonathan Davis, Elizabeth A. Nathan, Linda Palumbo, Soumya Ghosh, Lakshmi Nagarajan, Shripada C. Rao
Early Human Development. 2020; 143: 105011
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234 Development and evaluation of a multiplex conventional reverse-transcription polymerase chain reaction assay for detection of common viral pathogens causing acute gastroenteritis
Suvrotoa Mitra, Mukti Kant Nayak, Agniva Majumdar, Avisek Sinha, Soumyadipta Chatterjee, Alok Deb, Mamta Chawla-Sarkar, Shanta Dutta
Diagnostic Microbiology and Infectious Disease. 2020; 97(4): 115061
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235 Evaluation of Escherichia coli as an indicator for antimicrobial resistance in Salmonella recovered from the same food or animal ceca samples
Epiphanie Nyirabahizi, Gregory H. Tyson, Uday Dessai, Shaohua Zhao, Claudine Kabera, Emily Crarey, Niketta Womack, Mary Katherine Crews, Errol Strain, Heather Tate
Food Control. 2020; 115: 107280
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236 The flash visual evoked potential-P2 and the detection of amnestic mild cognitive impairment: A review of empirical literature
James E. Arruda, Madison C. McInnis, Jessica Steele
International Journal of Psychophysiology. 2020; 155: 162
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237 Early determination of mildew status in storage maize kernels using hyperspectral imaging combined with the stacked sparse auto-encoder algorithm
Dong Yang, Jianghao Yuan, Qing Chang, Huiyi Zhao, Yang Cao
Infrared Physics & Technology. 2020; 109: 103412
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238 Predictive accuracy of the Post-Stroke Depression Prediction Scale: A prospective binational observational study?
Julian Hirt, Lianne C.J. van Meijeren, Susanne Saal, Thóra B. Hafsteinsdóttir, Jeannette Hofmeijer, Andrea Kraft, Gabriele Meyer, Janneke M. de Man-van Ginkel
Journal of Affective Disorders. 2020; 265: 39
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239 Adsorption of catechol and hydroquinone on titanium oxide and iron (III) oxide
Mohd Kotaiba Abugazleh, Benjamin Rougeau, Hashim Ali
Journal of Environmental Chemical Engineering. 2020; 8(5): 104180
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240 Cut-off points for Polish-language versions of depression screening tools among patients with Type 2 diabetes
Andrzej Kokoszka, Ewelina Cichon, Marcin Obrebski, Andrzej Kiejna, Beata Rajba
Primary Care Diabetes. 2020; 14(6): 663
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241 DXA reference values and anthropometric screening for visceral obesity in Western Australian adults
Jonathan M. D. Staynor, Marc K. Smith, Cyril J. Donnelly, Amar El Sallam, Timothy R. Ackland
Scientific Reports. 2020; 10(1)
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242 Development of label-free gold nanoparticle based rapid colorimetric assay for clinical/point-of-care screening of cervical cancer
Tejaswini Appidi, Sushma V. Mudigunda, Suseela Kodandapani, Aravind Kumar Rengan
Nanoscale Advances. 2020; 2(12): 5737
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243 Prediction of traumatic pathology by classifying thorax trauma using a hybrid method for emergency services
Abdulkadir Karaci, Osman Ozkaraca, Ethem Acar, Ahmet Demir
IET Signal Processing. 2020; 14(10): 754
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244 Validity of the Multiple Auditory Processing Assessment–2: A Test of Auditory Processing Disorder
Ronald L. Schow, Mary M. Whitaker, J. Anthony Seikel, Jeff E. Brockett, Deborah M. Domitz Vieira
Language, Speech, and Hearing Services in Schools. 2020; 51(4): 993
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245 Evaluation of two (02) platforms for chemiluminescence-based detection of anti-rubella IgG antibodies in a sub-Saharan country, Côte d’Ivoire.
Bamory Dembele, Aimé Cézaire Adiko, Roseline Affi-Aboli, Rodrigue Denis Kouame, Kady Mabity Bamba, Jea-Luc Adjoumani, Tano Matthieu Kabran, André Inwoley
Journal of Immunoassay and Immunochemistry. 2020; 41(5): 864
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246 Concordance assessment between self-reports of substance use and urinalysis: A population-based study in Mashhad, Iran
Mohammad Khajedaluee, Seyed Abdolrahim Rezaee, Narges Valizadeh, Tahereh Hassannia, Toktam Paykani
Journal of Ethnicity in Substance Abuse. 2020; : 1
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247 Validation of Addiction Severity Index (ASI) for Assessment of Psychiatric Comorbidity in Multi-Site Randomized Controlled Trials
Ryoko Susukida, Ramin Mojtabai, Masoumeh Amin-Esmaeili
Journal of Dual Diagnosis. 2020; 16(3): 312
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248 Comparison of screening methods for obstructive sleep apnea in the context of dental clinics: A systematic review
Cecilia Rossi, Laura Templier, Manuel Miguez, Javier De La Cruz, Adrián Curto, Alberto Albaladejo, Manuel Lagravère Vich
CRANIO®. 2020; : 1
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249 Evaluation and simulation of breast cancer imaging devices using multi-criteria decision theory
H. Erdagli, D. Uzun Ozsahin, B. Uzun
Journal of Instrumentation. 2020; 15(05): C05029
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250 Leveraging machine learning for predicting flash flood damage in the Southeast US
Atieh Alipour, Ali Ahmadalipour, Peyman Abbaszadeh, Hamid Moradkhani
Environmental Research Letters. 2020; 15(2): 024011
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251 French-Canadian translation of a self-report questionnaire to monitor opioid therapy for chronic pain: The Opioid Compliance Checklist (OCC-FC)
Clarice Poirier, Marc O. Martel, Mélanie Bérubé, Aline Boulanger, Céline Gélinas, Line Guénette, Anaïs Lacasse, David Lussier, Yannick Tousignant-Laflamme, M. Gabrielle Pagé
Canadian Journal of Pain. 2020; 4(1): 59
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252 Additional CTA-Subtraction Technique in Detection of Pulmonary Embolism—a Benefit for Patients or Only an Increase in Dose?
Kai Nestler, Benjamin Valentin Becker, Matthäus Majewski, Daniel Anton Veit, Bastian Felix Krull, Stephan Waldeck
Health Physics. 2020; 119(1): 148
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253 Prediction of Nephrotoxicity Associated With Cisplatin-Based Chemotherapy in Testicular Cancer Patients
Sara L Garcia, Jakob Lauritsen, Zeyu Zhang, Mikkel Bandak, Marlene D Dalgaard, Rikke L Nielsen, Gedske Daugaard, Ramneek Gupta
JNCI Cancer Spectrum. 2020; 4(3)
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254 Assessing the validity of and factors that influence accurate self-reporting of HIV status after testing: a population-based study
Steady J.D. Chasimpha, Estelle M. Mclean, Albert Dube, Valerie McCormack, Isabel dos-Santos-Silva, Judith R. Glynn
AIDS. 2020; 34(6): 931
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255 Bimodal Automated Carotid Ultrasound Segmentation Using Geometrically Constrained Deep Neural Networks
Carl Azzopardi, Kenneth P. Camilleri, Yulia A. Hicks
IEEE Journal of Biomedical and Health Informatics. 2020; 24(4): 1004
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256 Offender personality disorder pathway screening tools evaluation
Zoe Mawby, Andrew Newman, Megan Wilkinson-Tough
The Journal of Forensic Practice. 2020; 22(3): 199
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257 Exploring dental students’ knowledge of HIV and attitudes towards saliva screening for HIV
Chui Yi Sarah Low, Sung-Beom Kim, Cyril Liu, Nicole Stormon
European Journal of Dental Education. 2020; 24(3): 483
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258 A supervised learning approach for heading detection
Sahib Singh Budhiraja, Vijay Mago
Expert Systems. 2020; 37(4)
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259 Performance of the Sysmex White Precursor Channel to discover circulating leukemic blast cells
Jesper Sejrup, David M. Pedersen, Jens P. Phillipsen, Jesper Ø. Nielsen, Sheila P. R. Koch, Julie Smith
International Journal of Laboratory Hematology. 2020; 42(6): 734
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260 Comparative evaluation of different antigen detection methods for the detection of peste des petits ruminants virus
Sabrina Halecker, Sunitha Joseph, Rubeena Mohammed, Ulrich Wernery, Thomas C. Mettenleiter, Martin Beer, Bernd Hoffmann
Transboundary and Emerging Diseases. 2020; 67(6): 2881
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261 A simple, refined approach to diagnosing renovascular hypertension in children: A 10-year study
Ken Saida, Koichi Kamei, Riku Hamada, Takahisa Yoshikawa, Yuji Kano, Hiroko Nagata, Mai Sato, Masao Ogura, Ryoko Harada, Hiroshi Hataya, Osamu Miyazaki, Shunsuke Nosaka, Shuichi Ito, Kenji Ishikura
Pediatrics International. 2020; 62(8): 937
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262 Equations To Predict Antimicrobial MICs in Neisseria gonorrhoeae Using Molecular Antimicrobial Resistance Determinants
Walter Demczuk, Irene Martin, Pam Sawatzky, Vanessa Allen, Brigitte Lefebvre, Linda Hoang, Prenilla Naidu, Jessica Minion, Paul VanCaeseele, David Haldane, David W. Eyre, Michael R. Mulvey
Antimicrobial Agents and Chemotherapy. 2020; 64(3)
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263 New Diagnosis Test under the Neutrosophic Statistics: An Application to Diabetic Patients
Muhammad Aslam, Osama H. Arif, Rehan Ahmad Khan Sherwani
BioMed Research International. 2020; 2020: 1
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264 Validation of mobile-based funduscope for diabetic retinopathy screening in Estonia
Birgit Krieger, Riina Hallik, Kristina Kala, Karina Ülper, Margarita Polonski
European Journal of Ophthalmology. 2020; : 1120672120
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265 Pro-cathepsin D as a diagnostic marker in differentiating malignant from benign pleural effusion: a retrospective cohort study
Hayoung Choi, Yousang Ko, Chang Youl Lee
BMC Cancer. 2020; 20(1)
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266 Eating disorder diagnostics in the digital era: validation of the Norwegian version of the Eating Disorder Assessment for DSM-5 (EDA-5)
Camilla Lindvall Dahlgren, B. Timothy Walsh, Karianne Vrabel, Cecilie Siegwarth, Øyvind Rø
Journal of Eating Disorders. 2020; 8(1)
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267 Transitioning to digital first line intervention – validation of a brief online screener for early identification of a suspected eating disorder: study protocol
Emma Bryant, Jane Miskovic-Wheatley, Stephen Touyz, Ross D. Crosby, Eyza Koreshe, Li Cao, Sarah Maguire
Journal of Eating Disorders. 2020; 8(1)
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268 An Assessment of Circulating Chromogranin A as a Biomarker of Bronchopulmonary Neuroendocrine Neoplasia: A Systematic Review and Meta-Analysis
Anna Malczewska, Mark Kidd, Somer Matar, Beata Kos-Kudla, Lisa Bodei, Kjell Oberg, Irvin M. Modlin
Neuroendocrinology. 2020; 110(3-4): 198
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269 A smartphone microscopic method for simultaneous detection of (oo)cysts of Cryptosporidium and Giardia
Retina Shrestha, Rojina Duwal, Sajeev Wagle, Samiksha Pokhrel, Basant Giri, Bhanu Bhakta Neupane, Joseph Mathu Ndung'u
PLOS Neglected Tropical Diseases. 2020; 14(9): e0008560
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270 Visual attention outperforms visual-perceptual parameters required by law as an indicator of on-road driving performance
Wolfgang Grundler, Hans Strasburger, Sergio A. Useche
PLOS ONE. 2020; 15(8): e0236147
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271 A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction
Zhiyong Hu, Dongping Du, Lars Kaderali
PLOS ONE. 2020; 15(9): e0237724
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272 Diagnostic performance of RFLP-PCR and sarcosine based indirect ELISA versus immunoassays in Brucella infected and vaccinated small ruminants
S. M. Soliman, H. S. Soliman, H. I. Mohamed, M. A. Salem, S. A. Ahmed
BULGARIAN JOURNAL OF VETERINARY MEDICINE. 2020; 23(3): 319
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273 Zika virus serological diagnosis: commercial tests and monoclonal antibodies as tools
Isaura Beatriz Borges Silva, Aldacilene Souza da Silva, Mariana Sequetin Cunha, Aline Diniz Cabral, Kelly Cristina Alves de Oliveira, Elizabeth De Gaspari, Carlos Roberto Prudencio
Journal of Venomous Animals and Toxins including Tropical Diseases. 2020;
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274 Diagnostic Accuracy of Ultrasonography to Detect False Layers in a Commercial Laying Flock Infected by an Infectious Bronchitis Virus Delmarva Genotype Causing Cystic Oviducts
Eric Parent, Ariane Gagnon-Francoeur, Benoît Lanthier, Ghislain Hébert, Sébastien Buczinski, Martine Boulianne
Avian Diseases. 2020; 64(2): 149
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275 Therapeutic Index for Local Infections score validity: a retrospective European analysis
Joachim Dissemond, Robert Strohal, Diego Mastronicola, Eric Senneville, Cécile Moisan, Valerie Edward-Jones, Kirsty Mahoney, Adam Junka, Marzenna Bartoszewicz, José Verdú-Soriano
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276 Optimization of Lyophilized LAMP and RT-PCR Reaction Mixes for Detection of Tuberculosis
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277 Solutions of Ternary Problems of Conditional Probability with Applications to Mathematical Epidemiology and the COVID-19 Pandemic
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278 The Predictive Value of Serum Cytokines for Distinguishing Celiac Disease from Non-Celiac Gluten Sensitivity and Healthy Subjects
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279 Label-Free Classification of Apoptosis, Ferroptosis and Necroptosis Using Digital Holographic Cytometry
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280 Transient Expression of Dengue Virus NS1 Antigen in Nicotiana benthamiana for Use as a Diagnostic Antigen
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281 A Computation Method Based on the Combination of Chlorophyll Fluorescence Parameters to Improve the Discrimination of Visually Similar Phenotypes Induced by Bacterial Virulence Factors
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282 Waste Reduction Strategies: Factors Affecting Talent Wastage and the Efficacy of Talent Selection in Sport
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283 Psychometric Properties of the Generalized Anxiety Disorder-7 and Generalized Anxiety Disorder-Mini in United States University Students
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284 Quick, Single-Frequency Dielectric Characterization of Blood Samples of Pediatric Cancer Patients by a Cylindrical Capacitor: Pilot Study
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287 Detection of Antibodies against Mycobacterium bovis in Oral Fluid from Eurasian Wild Boar
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288 Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?
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290 Sperm Antioxidant Biomarkers and Their Correlation with Clinical Condition and Lifestyle with Regard to Male Reproductive Potential
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293 External Validation of Carbapenem-Resistant Enterobacteriaceae Acquisition Risk Prediction Model in a Medium Sized Hospital
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294 A Loop-Mediated Isothermal Amplification Assay for the Detection of Treponema pallidum subsp. pertenue
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295 DIAGNOSTIC ACCURACY OF FNAC VERSUS OPEN BIOPSY IN DIAGNOSING CARCINOMA BREAST IN PALPABLE ADULT FEMALE BREAST LESIONS IN POPULATION OF KHYBER PAKHTUNKHWA, PAKISTAN
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296 Validity of Diagnostic Codes for Identification of Psoriasis Patients in Korea
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298 Estimating Morphological Features of Plant Growth Using Machine Vision
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299 A predictive model to identify Kanban teams at risk
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300 5-Hydroxymethylcytosines in Circulating Cell-Free DNA Reveal Vascular Complications of Type 2 Diabetes
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301 Cholera Outbreak in Haiti
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306 Robust Postdonation Blood Screening Under Prevalence Rate Uncertainty
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307 Psychometric properties of apathy scales in Parkinson's disease: a systematic review
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309 DNA Modulates the Interaction of Genetically Engineered DNA-Binding Proteins and Gold Nanoparticles: Diagnosis of High-Risk HPV Infection
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312 Comparison of Existing Phenotypic and Genotypic Tests for the Detection of NDM and GES Carbapenemase- Producing Enterobacteriaceae
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313 Statistical-based approach in potential diagnostic application of urinary nucleosides in urogenital tract cancer
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315 Molecular Assay for Detection of Genetic Markers Associated with Decreased Susceptibility to Cephalosporins in Neisseria gonorrhoeae
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316 Whole-Genome Phylogenomic Heterogeneity of Neisseria gonorrhoeae Isolates with Decreased Cephalosporin Susceptibility Collected in Canada between 1989 and 2013
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317 An Assessment of Detection Canine Alerts using Flowers that Release Methyl Benzoate, the Cocaine Odorant, and an Evaluation of their Behavior in Terms of the VOCs Produced
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322 Evaluation of five different questionnaires for assessing sleep apnea syndrome in a sleep clinic
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323 Motor Evoked Potential Monitoring During Surgery of Middle Cerebral Artery Aneurysms: A Cohort Study
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325 Endocrine Disruptome—An Open Source Prediction Tool for Assessing Endocrine Disruption Potential through Nuclear Receptor Binding
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326 Methods for detecting circulating cancer stem cells (CCSCs) as a novel approach for diagnosis of colon cancer relapse/metastasis
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327 Comparison of Visible–Near Infrared and Short Wave Infrared hyperspectral imaging for the evaluation of rainbow trout freshness
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328 Evaluating the accuracy of a geographic closed-ended approach to ethnicity measurement, a practical alternative
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329 Developing and Evaluating the HRM Technique for Identifying Cytochrome P450 2D6 Polymorphisms
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330 Predicting driving ability using DriveSafe and DriveAware in people with cognitive impairments: A replication study
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336 The role of genetic polymorphisms in cytochrome P450 and effects of tuberculosis co-treatment on the predictive value of CYP2B6 SNPs and on efavirenz plasma levels in adult HIV patients
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339 Author reply
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340 Evaluation of a glaucoma patient
Thomas, R., Loibl, K., Parikh, R.
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341 Validation of a multifactorial risk factor model used for predicting future caries risk with nevada adolescents
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342 Enter the reverend: introduction to and application of Bayesæ theorem in clinical ophthalmology : Bayesæ theorem in clinical ophthalmology
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343 The STRATIFY tool and clinical judgment were poor predictors of falling in an acute hospital setting
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344 Chemical pathology case conference - Serum tumour markers
Poon, W.T., Yuen, Y.P., Mak, C.M., Chan, A.O.K., Chan, M.H.M., Chiu, R.W.K., Lam, C.W., (...), Chan, A.Y.W.
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345 The STRATIFY tool and clinical judgment were poor predictors of falling in an acute hospital setting
Joan Webster,Mary Courtney,Nicole Marsh,Catherine Gale,Belynda Abbott,Anita Mackenzie-Ross,Prue McRae
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346 Likelihood ratios: Clinical application in day-to-day practice
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347 Understanding and using sensitivity, specificity and predictive values.
Skaik, YA
Indian J Ophthalmol. 2008; 56(4): 341
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348 Authorsæ reply
Parikh, R., Mathai, A., Parikh, S., Sekhar, G., Thomas, R.
Indian Journal of Ophthalmology. 2008; 56(4): 341
[Pubmed]



 
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