Role of occupational exposures in lung cancer risk among women
Bibliographic record
Abstract
OBJECTIVES: To explore possible associations between selected occupational agents and lung cancer risk among women. METHODS: A population-based case-control study on lung cancer was conducted from 1996 to 2001 in Montreal, Canada. Cases were individuals diagnosed with incident lung cancer and population controls were randomly selected from electoral lists and frequency-matched to age and sex distributions of cases. Questionnaires on lifetime occupational history, smoking and demographic characteristics were collected during in-person interviews. As part of a comprehensive exposure assessment protocol, experts reviewed each subject's work history and assessed exposure to many agents. The current analysis, restricted to working women in the study, includes 361 cases and 521 controls. We examined the association between lung cancer and each of 22 occupational exposures, chosen because of their relatively high prevalences among these women. Each exposure was analysed in a separate multivariate logistic regression model, adjusted for smoking and other selected covariates. RESULTS: There were few elevated OR estimates between lung cancer and any of the agents, and none were statistically significant, although the limited numbers of exposed women engendered wide CIs. CONCLUSIONS: There was little evidence to suggest that women in this population had experienced excess risks of lung cancer as a result of their work exposures. However, the wide CIs preclude any strong inferences in this regard.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".