Body mass index and mortality from lung cancer in smokers and nonsmokers: A nationally representative prospective study of 220,000 men in China
Bibliographic record
Abstract
Low body mass index (BMI) has been associated with increased risk of lung cancer. However, the nature of the association, especially in populations with relatively low BMI, is less well characterized, as is the relevance to it of smoking. A nationally representative prospective cohort study included 217,180 Chinese men aged 40-79 years in 1990-91 who had no prior history of cancer and were followed up for 15 years. Standardized hazard ratios (HRs) were calculated for lung cancer mortality by baseline BMI. The mean baseline BMI was 21.7 kg/m(2), and 2,145 lung cancer deaths were recorded during 15 years of follow-up. The prevalence of smoking was strongly inversely associated with BMI, but no apparent relationship was seen between amount smoked (or other measures of smoking intensity) and BMI among smokers. Overall there was a strong inverse association between BMI and lung cancer mortality (p < 0.0001 for trend) after excluding the first 3 years of follow-up. This association appeared to be confined mainly to current smokers, with no apparent relationship in nonsmokers (p < 0.001 for difference between slopes). Among current smokers, the inverse association appeared to be log-linear, with each 5 kg/m(2) lower BMI associated with a 35% (95% confidence interval: 24-46%; p < 0.0001) higher lung cancer mortality, and it persisted after excluding those who had reported poor health status or history of any disease or respiratory symptoms at baseline. In this relatively lean Chinese male population, low BMI was strongly associated with increased risk of lung cancer only among current smokers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".