The occupational risk of tuberculosis in a low-prevalence population: Table 1.
Bibliographic record
Abstract
BACKGROUND: The incidence of tuberculosis (TB) infection in low-prevalence countries has been declining. Estimates of the risk of occupational TB in these countries are contradictory. AIMS: To evaluate the risk of occupational TB in a low-prevalence population using a comprehensive database. METHODS: All compensation claims in British Columbia (BC), Canada, reporting workplace TB exposure for the years 1999-2008 were reviewed. Cases with TB infection were identified for all occupational groups with five or more claims in the decade and analysis provided estimates of incidence rates of active TB and relative risks of latent TB (LTB) infections. RESULTS: There were 70 occupational groups making 639 claims including 100 with LTB and eight with active TB. Only 18 occupations had five or more claims. Four occupational groups had a significantly increased relative risk of infection compared with all other occupational groups. These were employment counsellors, registered nurses, x-ray technicians and home support workers. Active TB infections were relatively rare compared with the general population (1-4 compared with 7-10/100000 person-years, respectively). CONCLUSIONS: Few occupational groups were at risk of TB exposure at work on a regular basis. Only a handful had an apparent increased risk of contracting TB and these should be the focus of prevention efforts. Work-related active TB infections are rare hence the burden of occupational TB disease is low in BC.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".