Factors Associated with Tuberculin Conversion in Canadian Microbiology and Pathology Workers
Bibliographic record
Abstract
The risk of occupational tuberculosis (TB) infection and associated factors was estimated among all microbiology and pathology technicians and compared with a sample of nonclinical personnel in 17 Canadian acute care hospitals. Participants underwent tuberculin skin testing and completed questionnaires. Prior skin tests and vaccinations and all patients with TB hospitalized in the preceding 3 years were reviewed. Of the work areas where direction of air flow and air changes per hour were measured, only 51% were adequately ventilated. Among participating lab workers the average annual risk of tuberculin conversion was 1.0%. This was associated with lower hourly air exchange rates (16.7 versus 32.5 in workers with no conversion, p < 0.001) work in pathology (adjusted odds ratio [OR]: 5.4; [95% confidence interval: 1.3, 22], higher proportion of patients with missed diagnosis in the first 24 hours (per 20% increase-OR: 2.0; [1.3, 3.2], treatment delayed 1 week or more (per 20% increase-OR: 2.0; [3.2, 3.2]), and higher mortality (per 20% increase-OR: 2.5; [1.1, 5.6]). We conclude that laboratory workers, with no direct patient contact, have increased risk of tuberculin conversion in hospitals where a greater proportion of patients with TB die, or have delayed, or missed diagnosis, although this may be modified by workplace ventilation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".