Body Mass Index and Lung Cancer Risk in Women
Bibliographic record
Abstract
BACKGROUND: Studies have suggested that leanness in adulthood may be a risk factor for lung cancer; however, there is justifiable concern that the observed association may be due to residual confounding by smoking, preclinical weight loss, competing causes of death, or some combination of these. METHODS: To examine this association we used data from the Canadian National Breast Screening Study, which included 89,835 women ages 40-59 years at recruitment between 1980 and 1985. During a mean of 16 years of follow-up, we observed 750 incident lung cancer cases. We used Cox proportional hazards models to estimate hazard ratios and 95% confidence intervals for the association between body mass index (BMI) and lung cancer. RESULTS: After adjustment for pack-years of smoking and other covariates, there was some evidence for inverse associations in current smokers (hazard ratio for highest BMI quintile relative to the lowest = 0.63; 95% confidence interval = 0.48-0.83) and in former smokers (0.69; 0.39-1.23), whereas in never-smokers, BMI was positively associated with lung cancer (2.19; 1.00-4.80). The results for current and former smokers were not altered by exclusion of cases diagnosed within the first 5 years of follow-up; however, in never-smokers the strength of the association was reduced. CONCLUSIONS: The present study contributes to the aggregate evidence suggesting that there may be an inverse association between BMI and lung cancer among smokers. However, the contrasting pattern of associations between BMI and lung cancer seen in ever-smokers and never-smokers in this study requires explanation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".