Population-based asbestosis surveillance in British Columbia
Bibliographic record
Abstract
OBJECTIVES: To investigate the use of multiple health data sources for population-based asbestosis surveillance in British Columbia, Canada. METHODS: Provincial health insurance registration records, workers' compensation records, hospitalisation records, and outpatient medical service records were linked using individual-specific study identifiers. The study population was restricted to individuals > or = 15 years of age living in the province during 1992-2004. RESULTS: 1170 new asbestosis cases were identified from 1992 to 2004 for an overall incidence rate of 2.82 (men: 5.48, women: 0.23) per 100,000 population; 96% of cases were male and average (SD) age was 69 (10) years. Although the annual number of new cases increased by 30% during the surveillance period (beta = 2.36, p = 0.019), the observed increase in annual incidence rates was not significant (beta = 0.02, p = 0.398). Workers' compensation, hospitalisation and outpatient databases identified 23%, 48% and 50% of the total new cases, respectively. Of the new cases, 82% were identified through single data sources, 10% were only recorded in the workers' compensation records, and 36% only in each of the hospitalisation and outpatient records. 84% of hospitalisation cases and 83% of outpatient cases were not included in the workers' compensation records. The three data sources showed different temporal trends in the annual number of new cases and annual incidence rates. CONCLUSIONS: Single data sources were not sufficient to identify all new cases, thus leading to serious underestimations of the true burden of asbestosis. Integrating multiple health data sources could provide a more complete picture in population-based surveillance of asbestosis and other occupational diseases.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".