Effect Modification of the Association of Cumulative Exposure and Cancer Risk by Intensity of Exposure and Time Since Exposure Cessation: A Flexible Method Applied to Cigarette Smoking and Lung Cancer in the SYNERGY Study
Bibliographic record
Abstract
The indiscriminate use of the cumulative exposure metric (the product of intensity and duration of exposure) might bias reported associations between exposure to hazardous agents and cancer risk. To assess the independent effects of duration and intensity of exposure on cancer risk, we explored effect modification of the association of cumulative exposure and cancer risk by intensity of exposure. We applied a flexible excess odds ratio model that is linear in cumulative exposure but potentially nonlinear in intensity of exposure to 15 case-control studies of cigarette smoking and lung cancer (1985-2009). Our model accommodated modification of the excess odds ratio per pack-year of cigarette smoking by time since smoking cessation among former smokers. We observed negative effect modification of the association of pack-years of cigarette smoking and lung cancer by intensity of cigarette smoke for persons who smoked more than 20-30 cigarettes per day. Patterns of effect modification were similar across individual studies and across major lung cancer subtypes. We observed strong negative effect modification by time since smoking cessation. Application of our method in this example of cigarette smoking and lung cancer demonstrated that reducing a complex exposure history to a metric such as cumulative exposure is too restrictive.
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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 | 0.006 | 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 | 0.000 |
| 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".