Interferon-Gamma Release Assays for the Diagnosis of Latent Tuberculosis Infection in HIV-Infected Individuals: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To determine whether interferon-gamma release assays (IGRAs) improve the identification of HIV-infected individuals who could benefit from latent tuberculosis infection therapy. DESIGN: Systematic review and meta-analysis. METHODS: We searched multiple databases through May 2010 for studies evaluating the performance of the newest commercial IGRAs (QuantiFERON-TB Gold In-Tube [QFT-GIT] and T-SPOT.TB [TSPOT]) in HIV-infected individuals. We assessed the quality of all studies included in the review, summarized results in prespecified subgroups using forest plots, and where appropriate, calculated pooled estimates using random effects models. RESULTS: The search identified 37 studies that included 5736 HIV-infected individuals. In three longitudinal studies, the risk of active tuberculosis was higher in HIV-infected individuals with positive versus negative IGRA results. However, the risk difference was not statistically significant in the two studies that reported IGRA results according to manufacturer-recommended criteria. In persons with active tuberculosis (a surrogate reference standard for latent tuberculosis infection), pooled sensitivity estimates were heterogeneous but higher for TSPOT (72%; 95% confidence interval [CI], 62-81%) than for QFT-GIT (61%; 95% CI, 47-75%) in low-/middle-income countries. However, neither IGRA was consistently more sensitive than the tuberculin skin test in head-to-head comparisons. Although TSPOT appeared to be less affected by immunosuppression than QFT-GIT and the tuberculin skin test, overall, differences among the three tests were small or inconclusive. CONCLUSIONS: Current evidence suggests that IGRAs perform similarly to the tuberculin skin test at identifying HIV-infected individuals with latent tuberculosis infection. Given that both tests have modest predictive value and suboptimal sensitivity, the decision to use either test should be based on country guidelines and resource and logistic considerations.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 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".