Body mass index and lung cancer risk: a pooled analysis based on nested case-control studies from four cohort studies
Bibliographic record
Abstract
BACKGROUND: Obesity has been proposed as a potential protective factor against lung cancer. We examined the association between BMI and lung cancer risk in a pooled analysis based on nested case-control studies from four cohort studies. METHODS: ), and classified into 4 categories: underweight (BMI < 18.5), normal weight (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30) and obese (≥30). Odds ratios (ORs) and 95% confidence intervals (CIs) for BMI-lung cancer associations were estimated using unconditional logistic regression, adjusting for potential confounders. RESULTS: Considering all participants, and using normal weight as the reference group, a decreased risk of lung cancer was observed for those who were overweight (OR 0.77, 95% CI: 0.68-0.86) and obese (OR 0.69, 95% CI: 0.59-0.82). In the stratified analysis by smoking status, the decreased risk for lung cancer was observed among current, former and never smokers (P for interaction 0.002). The adjusted ORs for overweight and obese groups were 0.79 (95% CI: 0.68-0.92) and 0.75 (95% CI: 0.60-0.93) for current smokers, 0.70 (95% CI: 0.53-0.93) and 0.55 (95% CI: 0.37-0.80) for former smokers, 0.77 (95% CI: 0.59-0.99), and 0.71 (95% CI: 0.44-1.14) for never smokers, respectively. While no statistically significant association was observed for underweight subjects who were current smokers (OR 1.24, 95% CI: 0.98-1.58), former smokers (OR 0.27, 95% CI: 0.12-0.61) and never smokers (OR 0.83, 95% CI: 0.5.-1.28). CONCLUSION: The results of this study provide additional evidence that obesity is associated with a decreased risk of lung cancer. Further biological studies are needed to address this association.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".