Interferon-γ Release Assays for Active Pulmonary Tuberculosis Diagnosis in Adults in Low- and Middle-Income Countries: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: The diagnostic value of interferon-γ release assays (IGRAs) for active tuberculosis in low- and middle-income countries is unclear. METHODS: We searched multiple databases for studies published through May 2010 that evaluated the diagnostic performance of QuantiFERON-TB Gold In-Tube (QFT-GIT) and T-SPOT.TB (T-SPOT) among adults with suspected active pulmonary tuberculosis or patients with confirmed cases in low- and middle-income countries. We summarized test performance characteristics with use of forest plots, hierarchical summary receiver operating characteristic (HSROC) curves, and bivariate random effects models. RESULTS: Our search identified 789 citations, of which 27 observational studies (17 QFT-GIT and 10 T-SPOT) evaluating 590 human immunodeficiency virus (HIV)-uninfected and 844 HIV-infected individuals met inclusion criteria. Among HIV-infected patients, HSROC/bivariate pooled sensitivity estimates (highest quality data) were 76% (95% confidence interval [CI], 45%-92%) for T-SPOT and 60% (95% CI, 34%-82%) for QFT-GIT. HSROC/bivariate pooled specificity estimates were low for both IGRA platforms among all participants (T-SPOT, 61% [95% CI, 40%-79%]; QFT-GIT, 52% [95% CI, 41%-62%]) and among HIV-infected persons (T-SPOT, 52% [95% CI, 40%-63%]; QFT-GIT, 50% [95% CI, 35%-65%]). There was no consistent evidence that either IGRA was more sensitive than the tuberculin skin test for active tuberculosis diagnosis. CONCLUSIONS: In low- and middle-income countries, neither the tuberculin skin test nor IGRAs have value for active tuberculosis diagnosis in adults, especially in the context of HIV coinfection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.026 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".