O8A.1 Estimating the burden of lung cancer due to occupational exposure to radon gas
Bibliographic record
Abstract
Background Radon is a well-known cause of lung cancer. Our goal was to estimate the prevalence and level of occupational exposure to radon, and to estimate the current lung cancer burden caused by radon exposure in Canadian workplaces. Methods Highly exposed (i.e. underground) workers were assigned exposure proportions at the national level using CAREX methodology. Exposure for the indoor working population was estimated using province-specific radon measurements from the Canadian federal building survey (n=12 870 samples). The proportion of workers exposed to specific ranges of radon (50–100, 100–150, 150–200, 200–400, 400–800,>800 Bq/m3) were calculated and we assigned the midpoint of the range as the average radon concentration for each exposure group. For the >800 Bq/m3category, the province-specific mean of measurements>800 Bq/m3was assigned. The above exposure assessment was applied to a population model of the historical Canadian labour force and exposures between 1961 and 2001 (the risk exposure period) were considered as contributing to cancer cases in 2011. The BEIR VI exposure-age-concentration model was used to assign relative risks by exposure category. The population attributable fraction was calculated using Levin’s equation. Results There were an estimated 4.4 million indoor workers and 26 000 highly exposed workers exposed to radon during the risk exposure period. Nearly 80% of these workers were exposed below 50 Bq/m3 (half the WHO reference level). Combining the indoor and highly exposed workers, we calculated that 0.80% of lung cancers are attributable to occupational radon exposure; this equates to 188 lung cancer cases per year. Conclusions Ours was the first study to use a data-driven approach to estimate radon exposure and lung cancer burden for indoor workers. Some of the attributable cases can be prevented by reducing workers’ exposure at workplace.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".