Smoking at time of diagnosis and breast cancer-specific survival: new findings and systematic review with meta-analysis
Bibliographic record
Abstract
INTRODUCTION: In women with breast cancer who smoke, it is unclear whether smoking could impair their survival from the disease. METHODS: We examined the relation of smoking at diagnosis to breast cancer-specific and overall survival among 5,892 women with invasive breast cancer treated in one Canadian center (1987 to 2008). Women were classified as never, former or current smokers. Current smokers were further classified according to total, intensity and duration of smoking. Deaths were identified through linkage to population mortality data. Cox proportional-hazards multivariate models were used. A systematic review with meta-analysis combines new findings with published results. RESULTS: Compared with never smokers, current smokers at diagnosis had a slightly, but not statistically significant, higher breast cancer-specific mortality (hazard ratio = 1.15, 95% confidence interval (CI): 0.97 to 1.37). Among current smokers, breast cancer-specific mortality increased with total exposure to, intensity and duration of smoking (all Ptrend <0.05). Compared to never smokers, breast cancer-specific mortality was 32 to 56% higher among heavy smokers (more than 30 pack years of smoking, more than 20 cigarettes per day or more than 30 years of smoking). Smoking at diagnosis was associated with an increased all-cause mortality rate. A meta-analysis of all studies showed a statistically significant, 33% increased mortality from breast cancer in women with breast cancer who are smokers at diagnosis compared to never smokers (hazard ratio = 1.33, 95% CI: 1.12 to 1.58). CONCLUSIONS: Available evidence to date indicates that smoking at diagnosis is associated with a reduction of both overall and breast cancer-specific survival. Studies of the effect of smoking cessation after diagnosis on breast cancer-specific outcomes are needed.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".