Tobacco smoking as a risk factor of bronchioloalveolar carcinoma of the lung: pooled analysis of seven case–control studies in the International Lung Cancer Consortium (ILCCO)
Bibliographic record
Abstract
BACKGROUND: The International Lung Cancer Consortium (ILCCO) was established in 2004, based on the collaboration of research groups leading large molecular epidemiology studies of lung cancer that are ongoing or have been recently completed. This framework offered the opportunity to investigate the role of tobacco smoking in the development of bronchioloalveolar carcinoma (BAC), a rare form of lung cancer. METHODS: Our pooled data comprised seven case-control studies from the United States, with detailed information on tobacco smoking and histology, which contributed 799 cases of BAC and 15,859 controls. We estimated the odds ratio of BAC for tobacco smoking, using never smokers as a referent category, after adjustment for age, sex, race, and study center. RESULTS: The odds ratio of BAC for ever smoking was 2.47 (95% confidence interval [CI] 2.08, 2.93); the risk increased linearly with duration, amount, and cumulative cigarette smoking and persisted long after smoking cessation. The proportion of BAC cases attributable to smoking was 0.47 (95% CI 0.39, 0.54). CONCLUSIONS: This analysis provides a precise estimate of the risk of BAC for tobacco smoking.
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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 | 0.037 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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 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".