**Core Concept:** Sensitivity of a screening test measures the proportion of true positive results among all true positive and false negative results. A perfect screening test has a sensitivity of 100%.

**Why the Correct Answer is Right:** The correct answer (D) is derived from the formula for sensitivity (Se), which is calculated using the following steps:

1. **True Positive (TP):** The number of individuals with the disease who test positive.
2. **False Negative (FN):** The number of individuals without the disease who test negative.
3. **False Positive (FP):** The number of individuals without the disease who test positive.
4. **True Negative (TN):** The number of individuals without the disease who test negative.

**Why Each Wrong Option is Incorrect:**

A. False assumption: The formula is used to calculate the probability of disease based on test results. However, sensitivity describes the ability of the test to correctly identify those with the disease, not the probability of disease in the population.

B. Inaccurate formula: This option provides an incorrect formula for calculating sensitivity, making it incorrect.

C. Incomplete information: This option includes only one term (True Positive) without considering the necessary ratios.

D. Correct formula: This option correctly presents the formula to calculate sensitivity, which is essential for understanding the concept.

**Clinical Pearl / High-Yield Fact:** A high sensitivity (Se) value is desirable in screening tests, as it ensures that most affected individuals are detected. In contrast, a test with a high specificity (Sp) value ensures that most unaffected individuals are correctly identified as such. Striking a balance between sensitivity and specificity is crucial for optimal screening tests.

**Correct Answer:** D. Se = TP / (TP + FN)

**Explanation:** This formula is derived from the formula for sensitivity (Se):

Se = (TP / (TP + FN))

Where:
- TP (True Positive) is the number of individuals with the disease who test positive.
- FN (False Negative) is the number of individuals without the disease who test negative.

By dividing TP by the sum of TP and FN, we obtain the proportion of true positives out of all true positives and false negatives. A sensitivity of 100% means that all affected individuals are correctly identified as positive.