Body mass index, lifetime smoking intensity and lung cancer risk
Bibliographic record
Abstract
There is as yet no generally accepted explanation for the common finding that low body mass index (BMI) is associated with an increased risk of lung cancer. We investigated this association in a Canadian population-based case-control study (1996-2002) with a particular view to assessing the hypothesis that the observed association was due to residual confounding by smoking. Analyses were based on 1,076 cases and 1,439 controls who provided their height at enrollment and their weight at two points in time, at age 20 and 2 years before enrollment. BMI, in kg/m(2) , was classified into underweight (<18.5), normal (18.5-24.9), overweight (25.0-29.9), and obese (≥30). Smoking history was synthesized into a comprehensive smoking index (CSI) that integrated duration, intensity and time since quitting. Odds ratios (ORs) and 95% confidence intervals (CIs) for BMI-lung cancer associations were estimated, adjusting for CSI as well as several sociodemographic, lifestyle and occupational factors. The normal BMI category was used as the reference. Among those who were underweight at age 20, there was a lower risk of lung cancer (OR = 0.69, 95% CI: 0.50-0.95). Conversely, lung cancer risk was increased among those who were underweight 2 years before enrollment (OR = 2.30, 95% CI: 1.30-4.10). The results were almost identical when stratifying analyses based on smoking history into never/lighter and heavier smokers. The inverse association between recent BMI and lung cancer is unlikely to be largely attributable to residual confounding by smoking. Reverse causality or a true relationship between BMI and lung cancer remain plausible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".