**Core Concept**
Sensitivity is a statistical measure used in medicine to evaluate the performance of a diagnostic test. It represents the proportion of true positive results among all actual positives, essentially indicating how well the test can detect the presence of a disease.

**Why the Correct Answer is Right**
Sensitivity is calculated as the number of true positives divided by the sum of true positives and false negatives. This means it specifically measures the ability of a test to correctly identify individuals with the disease. In other words, a highly sensitive test will have fewer false negatives, thereby minimizing the likelihood of missing a true case.

**Why Each Wrong Option is Incorrect**
**Option A:** Specificity is the correct answer, not sensitivity. While related, specificity measures the proportion of true negatives among all actual negatives, indicating how well the test can rule out the disease.
**Option B:** Negative Predictive Value (NPV) is also not correct. NPV is the probability that a test result is negative in an individual without the disease, combining sensitivity and specificity.
**Option C:** Positive Predictive Value (PPV) is incorrect as well. PPV is the probability that a test result is positive in an individual with the disease, also combining sensitivity and specificity.

**Clinical Pearl / High-Yield Fact**
To remember the difference between sensitivity and specificity, recall that sensitivity is about "catching the disease" (true positives), whereas specificity is about "avoiding false alarms" (true negatives).

**Correct Answer: D. True positives among all actual positives.