Are we doing enough to identify and prioritise occupational carcinogens?
Bibliographic record
Abstract
This issue of Occupational and Environmental Medicine includes three excellent papers on the occupational causes of cancer. Loomis et al 1 use carcinogenicity evaluations completed by the International Agency for Research on Cancer (IARC) to characterise occupational exposures by tumour type, exposure scenarios and changing patterns of identification over time. Using the IARC classifications of occupational cancers, Marant Micallef et al 2 reviewed the literature to assemble the best-available relative risk estimates for each carcinogen–cancer site pair. These were used for an assessment of the total cancer burden from occupational exposures in France and can be used by other burden analyses. Jung et al 3 used the Occupational Disease Surveillance System in Ontario to calculate the relative contribution of occupational factors to the development of lung cancer in Canada. These papers1–3 remind us of the seminal influence that studies of occupational exposures have had on our understanding of the carcinogenic process, the progress in identifying these workplace hazards over the past several decades and their continued importance today. In 1981 the IARC had classified …
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.012 |
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".