Systematic Review and Meta-Analysis of Antigen Detection Tests for the Diagnosis of Tuberculosis
Bibliographic record
Abstract
Tests that detect Mycobacterium tuberculosis antigens in clinical specimens could provide rapid direct evidence of active disease. We performed a systematic review to assess the diagnostic accuracy of antigen detection tests for active tuberculosis (TB) according to standard methods and summarized test performance using bivariate random effects meta-analysis. Overall, study quality was a concern. For pulmonary TB (47 studies, 5,036 participants), sensitivity estimates ranged from 2% to 100% and specificity from 33% to 100%. Lipoarabinomannan (LAM) was the antigen most frequently targeted (23 studies, 49%). The pooled sensitivity of urine LAM was higher in HIV-infected than HIV-uninfected individuals (47%; 95% confidence interval [CI], 26 to 68% versus 14%; 95% CI, 4 to 38%); pooled specificity estimates were similar: 96%; 95% CI, 81 to 100% and 97%; 95% CI, 86 to 100%, respectively. For extrapulmonary TB (21 studies, 1,616 participants), sensitivity estimates ranged from 0% to 100% and specificity estimates from 62% to 100%. Five studies targeting LAM, ESAT-6, Ag85 complex, and the 65-kDa antigen in cerebrospinal fluid, when pooled, yielded the highest sensitivity (87%; 95% CI, 61 to 98%), but low specificity (84%; 95% CI, 60 to 95%). Because of the limited number of studies targeting any specific antigen other than LAM, we could not draw firm conclusions about the overall clinical usefulness of these tests. Further studies are warranted to determine the value of LAM detection for TB meningitis in high-HIV-prevalence settings. Considering that antigen detection tests could be translated into rapid point-of-care tests, research to improve their performance is urgently needed.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".