Priority Setting for Occupational Cancer Prevention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Selecting priority occupational carcinogens is important for cancer prevention efforts; however, standardized selection methods are not available. The objective of this paper was to describe the methods used by CAREX Canada in 2015 to establish priorities for preventing occupational cancer, with a focus on exposure estimation and descriptive profiles. METHODS: Four criteria were used in an expert assessment process to guide carcinogen prioritization: (1) the likelihood of presence and/or use in Canadian workplaces; (2) toxicity of the substance (strength of evidence for carcinogenicity and other health effects); (3) feasibility of producing a carcinogen profile and/or an occupational estimate; and (4) special interest from the public/scientific community. Carcinogens were ranked as high, medium or low priority based on specific conditions regarding these criteria, and stakeholder input was incorporated. Priorities were set separately for the creation of new carcinogen profiles and for new occupational exposure estimates. RESULTS: Overall, 246 agents were reviewed for inclusion in the occupational priorities list. For carcinogen profile generation, 103 were prioritized (11 high, 33 medium, and 59 low priority), and 36 carcinogens were deemed priorities for occupational exposure estimation (13 high, 17 medium, and 6 low priority). CONCLUSION: Prioritizing and ranking occupational carcinogens is required for a variety of purposes, including research, resource allocation at different jurisdictional levels, calculations of occupational cancer burden, and planning of CAREX-type projects in different countries. This paper outlines how this process was achieved in Canada; this may provide a model for other countries and jurisdictions as a part of occupational cancer prevention efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it