Modelling smoking history using a comprehensive smoking index: application to lung cancer
Bibliographic record
Abstract
The mathematical representation of smoking history is an important tool in analysis of epidemiological and clinical data. Hoffmann and colleagues recently proposed a single aggregate measure of smoking exposure that incorporates intensity, duration, and time since cessation. This comprehensive smoking index (CSI), which may be incorporated in any regression model, depends on a half-life (tau) and a lag (delta) parameters that have to be fixed a priori, or estimated by maximizing the fit. The CSI has not previously been used for analysis of cancer data. Following some preliminary results on smoking and lung cancer, the authors proposed a new version of the CSI for lung cancer. The aim of this study was to investigate the performance of the original and the new versions of the CSI in the analysis of three data sets from two case-control studies of lung cancer undertaken in Montreal, in 1979-1985 in males, and in 1996-2000 in both males and females. The estimates of tau and delta for both versions of the CSI were similar across data sets. The new version of the CSI fitted the three data sets systematically although moderately better than the original version, and at least as well as other representations of lifetime smoking history that used separate variables for time since cessation and cumulative amount of cigarettes smoked. The results suggest that the CSI may be an attractive and parsimonious alternative to conventional modelling of different aspects of smoking history for lung cancer.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".