0284 Carex: an occupational exposure surveillance system overview
Bibliographic record
Abstract
The CAREX system for occupational carcinogen exposure surveillance was developed by the Finnish Institute of Occupational Health in collaboration with IARC and European exposure experts in the early 1990s, and was shortly thereafter adapted for use in approximately 15 other countries in the European Union. The original platform (developed for Microsoft Access) allowed for assignment of exposure proportions by 55 industry categories, but was a breakthrough at the time in terms of amalgamating exposure measurement data and occupational hygiene knowledge surrounding carcinogen exposure. Recognising the importance of CAREX in occupational cancer prevention, several other countries around the world have since adapted the original system for use in their own countries, with a few making large improvements to the model. A notable example is Costa Rica, with their TICAREX adaptation that estimated pesticide exposure for the first time, and considered sex as an exposure-defining feature in workplaces. In Canada, the system was expanded further to consider exposure by hundreds of detailed industry and occupation codes, sex, sub-geographical regions within a country, and level of exposure where possible. In addition, an entirely new system for considering community environmental carcinogen exposures was added. The Canadian team has been working with the Pan American Health Organisation and other partners to expand the use of the enhanced CAREX to other countries, in particular those of lower and middle income, where capacity for new research and data structures may be difficult. After 25 years, the CAREX model continues to evolve and improve to meet current needs.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".