Influence of pre-diagnostic cigarette smoking on colorectal cancer survival: overall and by tumour molecular phenotype
Bibliographic record
Abstract
BACKGROUND: Smoking is a risk factor for incident colorectal cancer (CRC); however, it is unclear about its influence on survival after CRC diagnosis. METHODS: A cohort of 706 CRC patients diagnosed from 1999 to 2003 in Newfoundland and Labrador, Canada, was followed for mortality and recurrence until April 2010. Smoking and other relevant data were collected by questionnaire after cancer diagnosis, using a referent period of '2 years before diagnosis' to capture pre-diagnosis information. Molecular analyses of microsatellite instability (MSI) status and BRAF V600E mutation status were performed in tumour tissue using standard techniques. Multivariate hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated with Cox proportional hazards regression, controlling for major prognostic factors. RESULTS: Compared with never smokers, all-cause mortality (overall survival, OS) was higher for current (HR: 1.78; 95% CI: 1.04-3.06), but not for former (HR: 1.06; 95% CI: 0.71-1.59) smokers. The associations of cigarette smoking with the study outcomes were higher among patients with ≥40 pack-years of smoking (OS: HR: 1.72; 95% CI: 1.03-2.85; disease-free survival (DFS: HR: 1.99; 95% CI: 1.25-3.19), those who smoked ≥30 cigarettes per day (DFS: HR: 1.80; 95% CI: 1.22-2.67), and those with microsatellite stable (MSS) or MSI-low tumours (OS: HR: 1.38; 95% CI: 1.04-1.82 and DFS: HR: 1.32; 95% CI: 1.01-1.72). Potential heterogeneity was noted for sex (DFS HR: 1.68 for men and 1.01 for women: P for heterogeneity=0.04), and age at diagnosis (OS: HR: 1.11 for patients aged <60 and 1.69 for patients aged ≥60: P for heterogeneity=0.03). CONCLUSIONS: Pre-diagnosis cigarette smoking is associated with worsened prognosis among patients with CRC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".