Progression to tuberculosis disease increases with multiple exposures
Bibliographic record
Abstract
During a single year, a Canadian village had 34 individuals with microbiologically confirmed tuberculosis (TB) among 169 people with a new infection (20%). A contact investigation revealed multiple exposures for each person. We investigated whether the intensity of exposure might contribute to this extraordinary risk of disease.We carried out a case-control study using a public health database. Among those with a new infection, 34 had culture-confirmed TB (cases) and 118 did not progress to disease (controls). 17 patients with probable disease were excluded. Contact investigation data were utilised to tabulate the number of potential sources (total exposures). Generalised estimating equations with a logit link were used to identify associations between exposures and progression, and to investigate other potential risk factors.The median (interquartile range) number of total exposures was 15 (3-23) for cases and 3 (2-12) for controls (p=0.001). The adjusted OR for disease was 1.11 (95% CI 1.06-1.16) per additional exposure, corresponding to an OR of 3.4 for disease when comparing the medians of 15 versus 3 total exposures. This association increased when restricting to tuberculin skin test conversions.Increased exposure could be a marker of greater risk of progression to TB disease. Therefore, this risk may not be transportable across epidemiologic settings with variable exposure intensities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".