Risk of lung cancer in relation to various metrics of smoking history: a case-control study in Montreal
Bibliographic record
Abstract
BACKGROUND: Few epidemiologic findings are as well established as the association between smoking and lung cancer. It is therefore somewhat surprising that there is not yet a clear consensus about the exposure-response relationships between various metrics of smoking and lung cancer risk. In part this is due to heterogeneity of how exposure-response results have been presented and the relative paucity of published results using any particular metric of exposure. The purposes of this study are: to provide new data on smoking-lung cancer associations and to explore the relative impact of different dimensions of smoking history on lung cancer risk. METHODS: Based on a large lung cancer case-control study (1203 cases and 1513 controls) conducted in Montreal in 1996-2000, we estimated the lifetime prevalence of smoking and odds ratios in relation to several smoking metrics, both categorical and continuous based on multivariable unconditional logistic regression. RESULTS: Odds ratios (ORs) for ever vs never smoking were 7.82 among males and 11.76 among females. ORs increased sharply with every metric of smoking examined, more so for duration than for daily intensity. In models using continuous smoking variables, all metrics had strong effects on OR and mutual adjustment among smoking metrics did not noticeably attenuate the OR estimates, indicating that each metric carries some independent risk-related information. Among all the models tested, the one based on a smoking index that integrates several smoking dimensions, provided the best fitting model. Similar patterns were observed for the different histologic types of lung cancer. CONCLUSIONS: This study provides many estimates of exposure-response relationships between smoking and lung cancer; these can be used in future meta-analyses. Irrespective of the histologic type of lung cancer and the smoking metric examined, high levels of smoking led to high levels of risk, for both men and women.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".