Cigarette Smoking and Cancer Risk: Modeling Total Exposure and Intensity
Bibliographic record
Abstract
A recent analysis showed that the excess odds ratio (EOR) for lung cancer due to smoking can be modeled by a function which is linear in total pack-years and exponential in the logarithm of smoking intensity and its square. Below 15-20 cigarettes per day, the EOR/pack-year increased with intensity (direct exposure rate or enhanced potency effect), suggesting greater risk for a total exposure delivered at higher intensity (for a shorter duration) than for an equivalent exposure delivered at lower intensity. Above 20 cigarettes per day, the EOR/pack-year decreased with increasing intensity (inverse exposure rate or reduced potency effect), suggesting greater risk for a total exposure delivered at lower intensity (for a longer duration) than for an equivalent exposure delivered at higher intensity. The authors applied this model to data from 10 case-control studies of cancer, including cancers of the lung, bladder, oral cavity, pancreas, and esophagus. At lower intensities, there was enhanced potency for several cancer sites, but narrow ranges for pack-years increased uncertainty, precluding definitive conclusions. At higher intensities, there was a consistent reduced potency effect across studies. The intensity effects were statistically homogeneous, indicating that after accounting for risk from total pack-years, intensity patterns were comparable across the diverse cancer sites.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".