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Diagnostic Test Basics

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Diagnostic Test Basics

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Core concepts of diagnostic testing

Created by

yuly Ney Burgos Ballesteros
yuly Ney Burgos Ballesteros
Colombia

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Diagnostic Test Basics
 

Diagnostic Test BasicsOnline version

Core concepts of diagnostic testing

by yuly Ney Burgos Ballesteros
1

What is sensitivity in a diagnostic test?

2

What does specificity measure?

3

A test with high sensitivity is best for:

4

A highly specific test is best for:

5

Positive predictive value depends on:

6

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

7

What is the false negative rate?

8

What is the false positive rate?

9

ROC curve helps to choose:

10

Prevalence affects which metric most?

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Sensitivity = true positives / (true positives + false negatives)

Specificity = true negatives / (true negatives + false positives)

High sensitivity minimizes false negatives, good for ruling out.

High specificity minimizes false positives, good for ruling in.

PPV rises with higher prevalence and better test performance.

LR+ = sensitivity / (1 - specificity); boosts post-test probability.

False negative rate equals miss rate when disease is present.

False positives occur when test is positive despite no disease.

ROC plots sensitivity vs (1-specificity) at thresholds.

PPV increases with higher disease prevalence.

 
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