Latent <i>Mycobacterium tuberculosis</i> Infection and Interferon-Gamma Release Assays
Bibliographic record
Abstract
The identification of individuals with latent tuberculosis infection (LTBI) is useful for both fundamental understanding of the pathogenesis of disease and for clinical and public health interventions (i.e., to prevent progression to disease). Basic research suggests there is a pathogenetic continuum from exposure to infection to disease, and individuals may advance or reverse positions within the spectrum, depending on changes in the host immunity. Unfortunately, there is no diagnostic test that resolves the various stages within the spectrum of Mycobacterium tuberculosis infection. Two main immune-based approaches are currently used for identification of LTBI: the tuberculin skin test (TST) and the interferon-gamma release assay (IGRA). TST can use either the conventional purified protein derivative or more specific antigens. Extensive research suggests that both TST and IGRA represent indirect markers of M. tuberculosis exposure and indicates a cellular immune response to M. tuberculosis. The imperfect concordance between these two tests suggests that neither test is perfect, presumably due to both technical and biological reasons. Neither test can accurately differentiate between LTBI and active TB. Both IGRA and TST have low sensitivity in a variety of immunocompromised populations. Cohort studies have shown that both TST and IGRA have low predictive value for progression from infection to active TB. For fundamental applications, basic research is necessary to identify those at highest risk of disease with a positive TST and/or IGRA. For clinical applications, the identification of such biomarkers can help prioritize efforts to interrupt progression to disease through preventive therapy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".