Commercial Serological Tests for the Diagnosis of Active Pulmonary and Extrapulmonary Tuberculosis: An Updated Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Serological (antibody detection) tests for tuberculosis (TB) are widely used in developing countries. As part of a World Health Organization policy process, we performed an updated systematic review to assess the diagnostic accuracy of commercial serological tests for pulmonary and extrapulmonary TB with a focus on the relevance of these tests in low- and middle-income countries. METHODS AND FINDINGS: We used methods recommended by the Cochrane Collaboration and GRADE approach for rating quality of evidence. In a previous review, we searched multiple databases for papers published from 1 January 1990 to 30 May 2006, and in this update, we add additional papers published from that period until 29 June 2010. We prespecified subgroups to address heterogeneity and summarized test performance using bivariate random effects meta-analysis. For pulmonary TB, we included 67 studies (48% from low- and middle-income countries) with 5,147 participants. For all tests, estimates were variable for sensitivity (0% to 100%) and specificity (31% to 100%). For anda-TB IgG, the only test with enough studies for meta-analysis, pooled sensitivity was 76% (95% CI 63%-87%) in smear-positive (seven studies) and 59% (95% CI 10%-96%) in smear-negative (four studies) patients; pooled specificities were 92% (95% CI 74%-98%) and 91% (95% CI 79%-96%), respectively. Compared with ELISA (pooled sensitivity 60% [95% CI 6%-65%]; pooled specificity 98% [95% CI 96%-99%]), immunochromatographic tests yielded lower pooled sensitivity (53%, 95% CI 42%-64%) and comparable pooled specificity (98%, 95% CI 94%-99%). For extrapulmonary TB, we included 25 studies (40% from low- and middle-income countries) with 1,809 participants. For all tests, estimates were variable for sensitivity (0% to 100%) and specificity (59% to 100%). Overall, quality of evidence was graded very low for studies of pulmonary and extrapulmonary TB. CONCLUSIONS: Despite expansion of the literature since 2006, commercial serological tests continue to produce inconsistent and imprecise estimates of sensitivity and specificity. Quality of evidence remains very low. These data informed a recently published World Health Organization policy statement against serological tests. Please see later in the article for the Editors' Summary.
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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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".