(A/A+C) x 100 in the given table isTest resultPersons with diseasePersons without diseaseTotalPositiveABA+BNegativeCDC + D
**Question:** In the given table, the test result is calculated as (A/A+C) x 100. Test result represents:
| Test Result | Persons with disease | Persons without disease | Total |
|----------------------------|----------------------|-------------------------|-------|
| A / (A+C) x 100 | 10 | 20 | 30 |
Core Concept:
The given formula (A / (A+C) x 100) represents the sensitivity of a diagnostic test. Sensitivity refers to the proportion of true positive results (i.e., correctly identifying individuals with the disease). In this formula, "A" represents the number of true positive results and "C" represents the total number of cases with and without the disease.
Why the Correct Answer is Right:
The correct answer is **D**, which represents the sensitivity of the test as follows:
1. The test result in the table shows a sensitivity of 40% (10/25). This means that out of the 10 individuals who tested positive, 4 had the disease (true positive), while 6 had no disease (false positive).
2. The formula (A / (A+C) x 100) is used to calculate sensitivity, where A is the number of true positive results and C is the total number of cases with and without the disease.
3. In this example, the formula gives us the sensitivity as (10/25) x 100 = 40%.
Why Each Wrong Option is Incorrect:
Option A: False positive rate (100 - Sensitivity)
Option B: False negative rate (100 - Sensitivity)
Option C: False positive rate = (False positive rate)
Option D: False negative rate = (False negative rate)
Clinical Pearl:
Understanding the sensitivity of a test is crucial when interpreting test results, especially in making clinical decisions. Sensitivity represents the ability of a test to detect true positive cases (individuals with the disease). A high sensitivity is desirable when the focus is on identifying those with the disease, while a low specificity is more important when the focus is on identifying those without the disease. This concept helps in clinical reasoning when interpreting test results and making decisions about patient management.