O-415 Changing trends for mesothelioma in Canada and their implications
Bibliographic record
Abstract
Introduction Canada was once the world’s largest producer of asbestos, but exposure has been decreasing since the 1970’s due to restrictions on use, lower occupational exposure limits, closing of mines, and a ban in 2018. Objectives The objectives of this study were to evaluate how rates of mesothelioma in Ontario and British Columbia (BC), which together constitute over 50% of the Canadian population, have changed over time, by sex, age, geographical region and tumour site. Methods The Ontario and BC Cancer Registries were used to identify 4,146 and 1,659 malignant mesothelioma cases between the years 1993–2017 and 1992–2016, respectively. Time trends were examined by sex, age, and anatomical site. Birth cohort models for Ontario were fit using US National Cancer Institute’s age-period-cohort analysis web tool. Results Ontario incidence rates for mesothelioma climbed from 1.0/100,000 in 1993 until 2012 when rates plateaued at approximately 1.6. In BC the rate climbed from 1.1 in 1993 to 1.7/100,000 in 2003, when it began to plateau. Although female rates are much lower than male, they continue to steadily rise in both provinces. Rates among people over the age of 70 rose dramatically over time, while rates were steady or dropped among people below the age of 50 in both provinces. Peritoneal rates continue to rise in Ontario, but not BC. Relative to the 1921–25 birth cohort, male incidence rate ratios increased until peaking in 1936–40. Rate ratios for subsequent male cohorts decreased. In contrast, using the same reference period, the risk in women rose slowly with successive birth cohorts, though confidence limits were wide due to the low case counts. Conclusion These complex changes over time may be due to major reductions in exposure in the 1970’s, longer latency periods associated with lower levels of exposure, and the growing importance of environmental exposures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".