SKINJEM: A JOB EXPOSURE MATRIX FOR OCCUPATIONAL SKIN CANCER RISK
Bibliographic record
Abstract
Background and Aims: Estimating occupational exposure to skin carcinogens at a population level is challenging due to a lack of specific tools to do so. The objective of this project is to create a job exposure matrix for skin carcinogens (SkinJEM), flagging industries and jobs with high potential for exposure in Canada. Methods: SkinJEM is a 2-dimensional matrix created with a standard coding system (n=520) on one axis and exposure categories on the other. Exposures were selected from the CAREX Canada database and IARC monographs. Jobs were flagged as exposed if ≥25% of workers were likely to be exposed. SkinJEM also includes a flag for ‘expert re-evaluation’, where exposures in a job differ by industry, or where there is textual information available. For situations where exposure is more industry-based than occupationally (i.e. arsenic exposure in wood preservation plants), data lines have been added to reflect this. There is also an indicator of confidence for each job line. Results: Eight compounds (solar, artificial, and ionizing radiation; PAHs, creosotes, mineral oil, coal-tars; arsenic) in 3 categories (radiation, petroleum-related, metals) were identified. All 520 unique 4-digit job codes were included in the matrix, in addition to all roll-ups to less specific codes (to accommodate varying data quality in epidemiologic studies). Of note, many jobs had expected exposure to several skin carcinogens, including welders in construction, roofers, medical staff in hospitals, and workers in utilities. Conclusions: Many Canadian workers are potentially exposed to agents that are known to or suspected of causing skin cancer. Through SkinJEM, we have found that these carcinogens may be encountered together in some workplaces. Further research is required to assess the skin toxicity of chemical/radiation mixtures. Next steps will include linkages of SkinJEM to cancer registries to examine risk for melanoma from occupational exposure to skin carcinogens.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".