How do I calculate sensitivity and specificity?
An example of an AI chatbot conversation about calculating sensitivity and specificity in medical tests.
How do I calculate sensitivity and specificity?
To calculate sensitivity, use the formula: Sensitivity = True Positives / (True Positives + False Negatives). For specificity, it's: Specificity = True Negatives / (True Negatives + False Positives).
Can you give me an example with numbers?
Sure! If a test has 80 true positives, 20 false negatives, 90 true negatives, and 10 false positives, then:
- Sensitivity = 80 / (80 + 20) = 0.80 or 80%
- Specificity = 90 / (90 + 10) = 0.90 or 90%.
What do these values mean in a clinical context?
Sensitivity indicates how well a test identifies those with the condition, while specificity shows how well it identifies those without it. High sensitivity is crucial for screening tests.
Are there any limitations to these metrics?
Yes, sensitivity and specificity don't account for prevalence or the clinical context. They can also be affected by the population being tested.
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