Risk factors for developing tuberculosis: a 12-year follow-up of contacts of tuberculosis cases.
Bibliographic record
Abstract
BACKGROUND: Many risk factors for the development of tuberculosis (TB) have been reported but have not been simultaneously assessed. OBJECTIVE: To determine the risk of developing TB associated with each risk factor, after adjusting for all others. METHODS: We performed a population-based, retrospective cohort study of the contacts of TB cases recorded in British Columbia, Canada. Known risk factors for the development of TB were assessed over a 12-year period; Cox regression was used to estimate the hazard ratios (HRs) of TB, adjusting for the other factors. RESULTS: Among 33 146 TB contacts, 228 developed TB during the study period (TB rate 668 per 100,000 population, 95%CI 604-783). The main risk factors for TB development were malnutrition (HR 37.5), no treatment of latent TB infection (HR 25) or <6 months of treatment (HR 5.38), age 0-10 years (HR 7.87), being a household contact (HR 8.47) and having a tuberculin skin test induration of >or=5 mm (HR >or=4.99). Bacille Calmette-Guérin vaccination significantly reduced the risk of TB development (HR 0.32, 95%CI 0.20-0.50). CONCLUSIONS: Among contacts of TB cases, we have identified the few factors that carry a very high risk for developing TB. These factors identify populations at highest risk and permit more effective TB control.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".