P-334 Occupational exposures experienced by Montréal women participating in two case-control studies
Bibliographic record
Abstract
Introduction Women constitute nearly half of the workforce. However, most of our knowledge about occupational diseases come from studies conducted on men. Objective To describe occupational exposures experienced by women. Methods Two case-control studies of postmenopausal breast cancer were conducted in Montreal in 1996 and in 2011. Questionnaires on lifetime occupational history were administered during in-person or telephone interviews. Experts reviewed subjects’ work history, assessing exposure to a list of 258 chemicals. Chemicals that were deemed to be present were categorized by concentration (‘low’, ‘medium’, ‘high’), where low represented a background occupational level and high was the highest level experienced in that work environment. We pooled exposure information from both studies by time period and by age of exposure. Results In both studies combined, the three most prevalent exposures were cleaning agents, ozone, and organic solvents; the jobs in which these top 3 agents were present included nurses and waitresses (cleaning agents), secretaries and clerks (ozone), and housekeepers and elementary school teachers (organic solvents). For cleaning agents and ozone, most exposures occurred at a low concentration (>98%) while slightly higher exposures to organic solvents were found (14% medium and 2% high). The top 3 agents by time period were: <1950, fabric dust, aliphatic aldehydes, and cotton dust; 1950–1969, cleaning agents, aliphatic aldehydes, and organic solvents; 1970–1989, cleaning agents, ozone, and aliphatic alcohols; and ≥1990, ozone, cleaning agents and aliphatic alcohols. The prevalence of exposures differed for women exposed earlier versus later in their working life (predominant agents ≤35 years of age: cleaning agents, aliphatic aldehydes, aliphatic alcohols; >35 years of age were ozone, cleaning agents, organic solvents). Conclusion Occupational exposures of women remain understudied; bringing out inconspicuous exposures can help better assess women’s occupational risks.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".