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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".