Diagnostic Test Basics

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Core concepts of diagnostic testing

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yuly Ney Burgos Ballesteros

Colombia

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Diagnostic Test Basics


Diagnostic Test BasicsVersion en ligne Core concepts of diagnostic testing par yuly Ney Burgos Ballesteros 1 What is sensitivity in a diagnostic test? a Probability the test is positive given disease is present b Probability the patient has disease given a positive result c Probability the test is positive given no disease d Probability of a false negative 2 What does specificity measure? a Probability the test is positive given disease is present b Overall accuracy of the test c Probability the patient has disease given a positive result d Probability the test is negative given no disease is present 3 A test with high sensitivity is best for: a Assess prevalence in population b Rule-out disease when negative c Rule-in disease when positive d Determine treatment effectiveness 4 A highly specific test is best for: a Rule-out disease when negative b Screen asymptomatic populations c Estimate disease prevalence d Rule-in disease when positive 5 Positive predictive value depends on: a Sensitivity only b Test cost c Specificity only d Disease prevalence in the population 6 What is a likelihood ratio for a positive result (LR+)? a 1 / (Sensitivity + Specificity) b (1 - Sensitivity) / Specificity c Sensitivity / (1 - Specificity) d Specificity / Sensitivity 7 What is the false negative rate? a 1 - PPV b 1 / Sensitivity c 1 - Sensitivity d 1 - Specificity 8 What is the false positive rate? a 1 / Specificity b 1 - Specificity c PPV d 1 - Sensitivity 9 ROC curve helps to choose: a Cost of testing b Optimal trade-off between sensitivity and specificity c Prevalence estimation d Treatment selection 10 Prevalence affects which metric most? a Positive Predictive Value (PPV) b Sensitivity c Likelihood Ratios d Specificity Feedback 1 Sensitivity = true positives / (true positives + false negatives) 2 Specificity = true negatives / (true negatives + false positives) 3 High sensitivity minimizes false negatives, good for ruling out. 4 High specificity minimizes false positives, good for ruling in. 5 PPV rises with higher prevalence and better test performance. 6 LR+ = sensitivity / (1 - specificity); boosts post-test probability. 7 False negative rate equals miss rate when disease is present. 8 False positives occur when test is positive despite no disease. 9 ROC plots sensitivity vs (1-specificity) at thresholds. 10 PPV increases with higher disease prevalence.

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Diagnostic Test Basics

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À propos de cette activité

Créé par

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Diagnostic Test BasicsVersion en ligne

par yuly Ney Burgos Ballesteros

What is sensitivity in a diagnostic test?

What does specificity measure?

A test with high sensitivity is best for:

A highly specific test is best for:

Positive predictive value depends on:

What is a likelihood ratio for a positive result (LR+)?

What is the false negative rate?

What is the false positive rate?

ROC curve helps to choose:

Prevalence affects which metric most?

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