Performance of the Tuberculin Skin Test and Interferon- Release Assays: An Update on the Accuracy, Cutoff Stratification, and New Potential Immune-based Approaches
Bibliographic record
Abstract
An association between biologic agents and reactivation of active disease from latent tuberculosis infection (LTBI) has been established. Screening for LTBI is, therefore, now recommended for candidates for biologic drugs. The tuberculin skin test (TST) and interferon-γ release assays (IGRA) are the available commercial tests for detecting LTBI. We discuss their accuracy in immune-competent subjects and patients with autoimmune diseases, as well as potential new approaches to immune diagnosis. IGRA seem to be more accurate than TST in bacillus Calmette-Guerin vaccinated subjects and patients with autoimmune diseases. However, longitudinal studies are needed to estimate the risk of progression to TB after IGRA-based and/or TST-based diagnosis of LTBI in these vulnerable patients. New tests are needed to identify those patients with LTBI who will develop active TB and need prophylaxis.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".