Interferon γ–Release Assays for Diagnosis of Latent Tuberculosis in Healthcare Workers in Low-Incidence Settings: Pros and Cons
Bibliographic record
Abstract
In well-resourced countries with a low incidence of tuberculosis (TB)16 (e.g., the US and Canada), a major focus of TB control efforts is the detection and treatment of latent TB infection (LTBI) to prevent reactivation to active TB disease. This approach is particularly relevant for healthcare workers (HCWs), and substantial resources are devoted to hospital TB-screening programs in low-incidence settings. Interferon γ–release assays (IGRAs), specifically the QuantiFERON-TB Gold In-Tube test (Cellestis) (QFT-GIT) and the T-SPOT.TB test (Oxford Immunotec), are used to detect and quantify the in vitro release of interferon γ from T cells stimulated by TB-specific antigens and can be used for LTBI diagnosis. Occupational-health and infection-control leaders in hospitals in low-incidence countries must decide whether to use IGRAs in their TB-screening programs in place of or in addition to the conventional tuberculin skin test (TST). This decision is complex, because IGRAs and the TST differ in terms of their costs and both their analytical and operational performance characteristics. Our understanding of the performance of these assays for baseline and serial testing in individuals with different risk factors for TB exposure and reactivation has been expanding rapidly. In this Q&A article, 5 experts in this field share their perspectives on the advantages and disadvantages of using IGRAs for screening HCWs for TB in settings of low TB incidence.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".