Latent <i>Mycobacterium tuberculosis</i> Infection and Interferon-Gamma Release Assays
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it