T-cell interferon-γ release assays for the rapid immunodiagnosis of tuberculosis: clinical utility in high-burden vs. low-burden settings
Bibliographic record
Abstract
PURPOSE OF REVIEW: The utility of T-cell interferon-gamma (IFN-gamma) responses to Mycobacterium tuberculosis specific antigens [interferon-gamma release assays (IGRAs)] in high-burden settings remains unclear and there is growing evidence that IGRA performance varies across high tuberculosis (TB) burden vs. low TB burden settings. Here we review the evidence supporting the utility of IGRAs in specific subgroups and compare their performance in high-burden vs. low-burden settings. RECENT FINDINGS: Although the IGRA, compared with the tuberculin skin test (TST), has greater specificity in BCG-vaccinated individuals, treatment of latent tuberculosis infection is not a priority in high-burden setting. Nevertheless, in high-burden settings, the TST performs reasonably well and correlates as well, or better, with proxy measures of exposure. SUMMARY: IGRAs may still be useful in high-burden settings in specific subgroups at high risk of progression, including young children, HIV-infected individuals and healthcare workers, but this requires confirmation. Although the IGRAs cannot distinguish between latent and active TB, their utility as rule-out tests, when combined with smear microscopy or the TST, requires further study. Prospective studies are required in high-burden settings to confirm whether IFN-gamma responses are predictive of high risk of progression to active TB, particularly in HIV-infected individuals.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".