Host Genomics and Control of Tuberculosis Infection
Bibliographic record
Abstract
Tuberculosis (TB), caused by the human pathogenic bacterium Mycobacterium tuberculosis, poses a major global health problem. The tubercle bacillus is transmitted from person to person by aerosol, but only a proportion of those in contact with infectious aerosol particles will become infected. If infection occurs, less than 10% of those infected will develop clinical signs of TB, while the majority will develop latent TB infection (LTBI). The identification and treatment of LTBI persons is a major aspect of TB control, especially in low-incidence, highly developed nations. In the absence of a gold standard test for latent TB, infection is inferred with the help of either the in vivo tuberculin skin test or in vitro interferon gamma release assays of anti-mycobacterial immunity. Recent work has observed high heritability of these immune assays indicating the critical role of the host genetic background on the establishment of infection and latency. Additional genetic studies have identified the host genetic background as an important covariate for the proper interpretation of the results obtained from LTBI assays. Taken together, these data suggest TB surveillance and control can likely be improved by including host genetic information into the interpretation of these widely used assays.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".