**Core Concept:** The specificity of a diagnostic test is a measure of how well the test excludes the disease when the result is negative. In other words, it is the proportion of true negatives among all negative test results.

**Why the Correct Answer is Right:** To calculate the specificity, we use the following formula: Specificity = (True Negatives / (True Negatives + False Positives)). In this case, we have the following data:

- Positive test results (True Positives) = 416
- Negative test results (True Negatives) = 104
- Total number of positive tests (Total Positive) = 425
- Total number of tests performed (Total) = 700

Plugging these values into the formula, we get:

Specificity = (104 / (104 + (416 / (180))) ≈ 75.9%

**Why Each Wrong Option is Incorrect:**

A. False positives is not a relevant factor in calculating specificity as specificity is concerned with excluding the disease (negative test results), not identifying it (positive test results).
B. The formula mentioned above is correct for calculating sensitivity, not specificity.
C. False negatives are also not a relevant factor in calculating specificity, as specificity is concerned with excluding the disease, not identifying it.

**Clinical Pearl:**

In clinical practice, specificity is an important parameter to consider when interpreting test results. A high specificity indicates that the test is likely to correctly identify the absence of the disease, which is crucial for ruling out a condition like myocardial infarction. However, sensitivity (the proportion of true positives among true positives) should also be considered when evaluating the overall performance of a test. A combination of high sensitivity and specificity is ideal for a diagnostic test.

**Correct Answer:**

A. 75.9%