In a 2x2 table, sensitivity is calculated as?
Show Answer & Explanation ▾
Sensitivity is the proportion of truly diseased people the test correctly identifies: true positives divided by all diseased subjects (true positives + false negatives).
Types of Assessment for Diagnosis is part of the Endocrine System section of Medicine. Practise 10 exam-style MCQs for NEET PG, FMGE, INI-CET and NEET SS, each with the correct answer and a detailed explanation.
This set turns on Types of Assessment for Diagnosis: how sensitivity, specificity and likelihood ratios are calculated, why predictive values shift with prevalence, what ROC curves and kappa measure, and how lead-time bias flatters screening — high-yield MCQs for NEET PG, FMGE and INI-CET.
Sensitivity is the proportion of truly diseased people the test correctly identifies: true positives divided by all diseased subjects (true positives + false negatives).
Specificity is the proportion of truly non-diseased people the test correctly identifies: true negatives divided by all non-diseased subjects (true negatives + false positives).
Predictive values depend on prevalence, unlike sensitivity and specificity. As prevalence falls, false positives make up a larger share of all positive results, so the positive predictive value falls.
Positive likelihood ratio = sensitivity / (1 - specificity) = 0.80 / 0.10 = 8. An LR+ above 10 is strong evidence for disease, 5-10 moderate, and 2-5 weak.
An AUC of 0.5 is the diagonal line of no discrimination, equivalent to chance. An AUC of 1.0 is perfect discrimination. Higher AUC means better overall ability to distinguish diseased from non-diseased across thresholds.
The reference standard is the best available method for establishing true disease status. When it is imperfect, misclassification of the reference itself distorts the new test's apparent accuracy, a problem known as verification bias.
Lead-time bias is the apparent survival gain from earlier diagnosis when the time of death is unchanged. Length-time bias is different: screening preferentially detects slow-progressing cases, which have better prognosis anyway.
Cohen kappa measures inter-rater agreement corrected for chance agreement: 1 is perfect agreement and 0 is agreement no better than chance. It is the standard measure for categorical diagnostic assessments.
This set turns on Types of Assessment for Diagnosis: how sensitivity, specificity and likelihood ratios are calculated, why predictive values shift with prevalence, what ROC curves and kappa measure, and how lead-time bias flatters screening — high-yield MCQs for NEET PG, FMGE and INI-CET.
MedLadder includes 10 curated MCQs for Types of Assessment for Diagnosis under Medicine (Endocrine System), covering NEET PG, FMGE, INI-CET and NEET SS preparation.