Identification of Occupational Cancer Risks in British Columbia
Bibliographic record
Abstract
Lifetime occupational histories as well as information on known and suspected breast cancer risk factors were collected by means of a self-administered questionnaire from 1018 women with incident breast cancer ascertained from the British Columbia Cancer Registry, and from 1020 population controls. A matched case-control study design was used. Conditional logistic regression for matched sets data and the likelihood ratio were used in a two-step procedure and were performed separately for pre-menopausal women, post-menopausal women, and for all cases combined. Excess risk was noted for several white-collar occupations. Significantly increased risk was observed: (1) among pre-menopausal women: in electronic data-processing operators; barbers and hairdressers; in sales and material processing occupations; and in the food, clothing, chemical and transportation industries; (2) among post-menopausal women: in schoolteaching; in medicine, health, and nursing occupations; in laundry and dry-cleaning occupations; and in the aircraft and automotive, including gasoline service station, industries. Several significant associations were also seen in the combined group of pre- and post-menopausal women, particularly in crop farmers and in the fruit and vegetable, publishing and printing, and motor vehicle repair industries. The results of this study suggest excess breast cancer risk in a number of occupations and industries, notably those that entail exposure to solvents and pesticides.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.001 | 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 teacher head, 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".