Risk of lung cancer associated with six types of chlorinated solvents: results from two case–control studies in Montreal, Canada: Table 1
Bibliographic record
Abstract
OBJECTIVES: To determine whether exposure to various chlorinated solvents is associated with lung cancer. METHODS: Two case-control studies of occupation and lung cancer were conducted in Montreal, and included 2016 cases and 2001 population controls. Occupational exposure to a large number of agents was evaluated using a combination of subject-reported job history and expert assessment. We examined associations between lung cancer among men and six specific chlorinated solvents and two chemical families (chlorinated alkanes and alkenes). ORs were calculated using unconditional multivariate logistic regression. RESULTS: When the two studies were pooled, there were indications of an increased risk of lung cancer associated with occupational exposure to perchloroethylene (OR(any exposure) 2.5, 95% CI 1.2 to 5.6; OR(substantial exposure) 2.4, 95% CI 0.8 to 7.7) and to carbon tetrachloride (OR(any exposure) 1.2, 95% CI 0.8 to 2.1; OR(substantial exposure) 2.5, 95% CI 1.1 to 5.7). No other chlorinated solvents showed both statistically significant associations and dose-response relationships. ORs appeared to be higher among non-smokers. When the lung cancer cases were separated by histological type, there was a suggestion of differential effects by tumour type, but statistical imprecision and multiple testing preclude strong inferences in this regard. CONCLUSIONS: There were suggestive, albeit inconsistent, indications that exposure to perchloroethylene and carbon tetrachloride may increase the risk of lung cancer. Results for other solvents were compatible with absence of risk.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".