The association between the incidence of postmenopausal breast cancer and occupational exposure to selected organic solvents, Montreal, Canada, 2008–2011
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most diagnosed cancer among women and recognized risk factors explain 25%-47% of cases. Organic solvents are used widely in the workplace and exposure may increase the risk of developing breast cancer, yet there are insufficient data to confirm this hypothesis. We sought to determine whether past occupational exposures to selected organic solvents were associated with the incidence of invasive breast cancer in postmenopausal women in Montréal, Canada. METHODS: From a population-based case-control study (2008-2011), using in-depth interviews we elicited information on risk factors and lifetime occupational histories. Industrial hygienists and chemists translated job descriptions into specific chemical and physical exposures. We assessed 11 individual solvents and four solvent groups. Unconditional logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for metrics of past exposures to selected solvents. Exposure metrics included any previous exposure, average frequency in hours per week, duration in years, and average cumulative concentration weighted by hours per workweek exposed. RESULTS: We enrolled 695 cases and 608 controls. We found increased ORs for average cumulative concentration of exposure to mononuclear aromatic hydrocarbons (OR: 1.52, 95% CI: 1.04, 2.28), chlorinated alkanes (OR: 2.42, 95% CI: 1.23, 5.68), toluene (OR: 1.59, 95% CI: 1.02, 2.59), and a group of organic solvents with reactive metabolites (OR: 1.53, 95% CI: 1.08, 2.24). Positive associations were found across all exposure metrics and were higher among women with estrogen-positive/progesterone-negative tumors. CONCLUSION: Our findings suggest occupational exposure to certain organic solvents may increase the risk of incident postmenopausal breast cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".