Accuracy and Utility of Commercially Available Amplification and Serologic Tests for the Diagnosis of Minimal Pulmonary Tuberculosis
Bibliographic record
Abstract
Diagnosis of patients with minimal active tuberculosis (TB) is difficult, as there is no single test with high sensitivity and specificity. The yield and clinical utility of a combination of diagnostic tests were prospectively studied among 500 consecutive patients referred for sputum induction for diagnosis of possible active TB. Patients underwent sputum induction, chest X-ray, tuberculin testing, and had blood drawn for serologic testing (Detect-TB test; Biochem ImmunoSystems). Sputum was examined with fluorescent microscopy and PCR (Amplicor MTB-Roche) and cultured for mycobacteria using liquid (BACTEC) and solid media. For the diagnosis of the 60 cases of active TB, sensitivity and specificity, respectively, of the following diagnostic tests were mycobacterial culture, 73% and 100%; PCR, 42% and 100%; chest X-ray, 67-77% and 66-76%; tuberculin testing, 94% and 20%; and serology, 33% and 87%. After consideration of PCR and radiographic and clinical characteristics, a positive serologic test was independantly associated with diagnosis of active disease (adjusted odds of disease if positive, 2.6; 95% confidence limits, 1.1,6.1). No currently available test has sensitivity and specificity high enough for the accurate diagnosis of minimal pulmonary TB. Utilization of a combination of tests, together with consideration of key clinical characteristics, could improve diagnostic accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".