A mother presents to the family physician with her 16-year-old son. She explains, "There's something wrong with him doc. His grades are getting worse, he's cutting class, he's gaining weight, and his eyes are often bloodshot." Upon interviewing the patient apart from his mother, he seems withdrawn and angry at times when probed about his social history. The patient denies abuse and sexual history. What initial test should be sent to rule out the most likely culprit of this patient's behavior?

Sexually transmitted infection (STI) testing

A 28-year-old male presents to his primary care physician with complaints of intermittent abdominal pain and alternating bouts of constipation and diarrhea. His medical chart is not significant for any past medical problems or prior surgeries. He is not prescribed any current medications. Which of the following questions would be the most useful next question in eliciting further history from this patient?

"Can you tell me more about the symptoms you have been experiencing?"

"Does the diarrhea typically precede the constipation, or vice-versa?"

"Is the diarrhea foul-smelling?"

"Please rate your abdominal pain on a scale of 1-10, with 10 being the worst pain of your life"

"Are the symptoms worse in the morning or at night?"

A pharmaceutical company develops a sequential testing protocol for a rare genetic disorder (prevalence 0.01%). Initial screening test has sensitivity 95% and specificity 90%. Positive results undergo confirmatory testing with sensitivity 99% and specificity 99.5%. The company claims this approach achieves PPV >80% for the final positive result. Evaluate this claim and the rationale for sequential testing in this context.

The claim is true; sequential testing increases PPV by enriching the population tested in the second step

The claim is false; sensitivity decreases with sequential testing, reducing PPV

Sequential testing is unnecessary; the first test alone achieves adequate PPV

The claim is false; sequential testing cannot achieve PPV >80% with such low prevalence

The claim is true; the high specificity of the confirmatory test ensures high PPV regardless of prevalence

A hospital system is implementing a sepsis screening algorithm using clinical criteria with sensitivity of 92% and specificity of 75%. False positives result in unnecessary antibiotics, cultures, and ICU evaluations costing $3,000 per case. Missing true sepsis cases (false negatives) results in average increased mortality and morbidity costs of $50,000 per case. Hospital sepsis prevalence is 8%. Evaluate the optimal threshold adjustment strategy.

Decrease threshold to improve sensitivity despite more false positives

Maintain current threshold as it balances sensitivity and specificity equally

Implement risk stratification with different thresholds for different populations

Abandon screening due to unacceptable false positive rate

Increase threshold to improve specificity and reduce costs from false positives

A 58-year-old man with chronic cough undergoes evaluation for tuberculosis. A tuberculin skin test (TST) is positive (15mm induration). TST has sensitivity of 80% and specificity of 95% in immunocompetent adults. However, he received BCG vaccination as a child in Asia. Local TB prevalence is 0.5%, but his occupational exposure increases his pre-test probability to 10%. Evaluate the most appropriate interpretation and management approach.

Calculate post-test probability and obtain interferon-gamma release assay (IGRA)

Repeat TST in 2 weeks to confirm

Treat empirically regardless of test characteristics

Positive test confirms TB; start treatment immediately

False positive due to BCG; no further testing needed

Specificity calculation and interpretation | Sensitivity/Specificity

Specificity - True Negative Ruler

Definition: The ability of a test to correctly identify individuals without a disease. It measures the proportion of true negatives among all healthy individuals.
Calculation:
- Formula: $Specificity = \frac{TN}{TN + FP}$
- TN: True Negatives (correctly identified as negative)
- FP: False Positives (incorrectly identified as positive)
Interpretation:
- A highly specific test, when positive, helps rule in the disease.
- 📌 Mnemonic: SP-IN (Specific test, Positive, rules IN)

⭐ High specificity is vital for confirmatory tests (e.g., Western blot for HIV). It minimizes false positives, preventing unnecessary anxiety and treatment for a disease a person does not have.

Sensitivity and Specificity Visualized

Interpretation - Avoiding False Alarms

High Specificity: A test's ability to correctly identify individuals without the disease (True Negative Rate).
When a highly specific test is positive, it helps rule in the disease.
- 📌 Mnemonic: SP-IN (Specific test, Positive result, rules IN).
Primary Goal: To minimize false positives. This is crucial when a false-positive result could lead to patient anxiety, stigma, and unnecessary, costly, or invasive follow-up procedures.
Reflects the formula: $Specificity = TN / (TN + FP)$
- A high value indicates a very low number of false positives.

⭐ High-yield fact: Confirmatory tests (e.g., Western blot for HIV after a positive ELISA screen) require high specificity to reliably verify a diagnosis and avoid the severe implications of a false-positive result.

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Calculation - Cracking the 2x2

Specificity quantifies a test's ability to correctly identify the "healthy" cohort-those without the disease. It essentially asks: "Of all the truly healthy individuals, what proportion tested negative?"
The 2x2 Table Framework:

	Disease Present	Disease Absent
Test Positive	True Positive (TP)	False Positive (FP)
Test Negative	False Negative (FN)	True Negative (TN)

-   Focus on the right-hand column (Disease Absent).
-   Formula: $Specificity = TN / (TN + FP)$
-   This represents the proportion of true negatives among all disease-free individuals.

Clinical Interpretation:
- A highly specific test minimizes false positives. If the test result is positive, you can be confident the person has the disease.

⭐ A positive result from a test with 99% specificity is highly likely to be a true positive, making it extremely useful for ruling in a diagnosis.

📌 Mnemonic: SP-IN → for a highly SPecific test, a Positive result helps to rule IN the disease.

High‑Yield Points - ⚡ Biggest Takeaways

Specificity measures a test's ability to correctly identify disease-free individuals.

Calculated as True Negatives / (True Negatives + False Positives).

A highly specific test, when positive, helps Rule IN the disease (SP-IN).

It is an intrinsic property of a test and is not affected by prevalence.

High specificity is crucial for confirmatory tests to minimize false-positive results.

Think of it as the "true negative rate" among all healthy individuals.