Association of cigarette smoking with the risk of ovarian cancer
Bibliographic record
Abstract
Cigarette smoking may be associated with ovarian cancer risk. This association may differ by histological type. The authors conducted a population-based case-control study in Canada of 442 incident cases of ovarian cancer and 2,135 controls 20-76 years of age during 1994-1997 to examine this association, overall and by histological type. Compared to women who never smoked, those who smoked had higher odds (odds ratio [OR] = 1.22; 95% confidence interval [CI] = 0.98-1.53) of having ovarian cancer, and the OR was larger for ex-smokers (1.30; 95% CI = 1.01-1.67) than for current smokers (1.10; 95% CI = 0.81-1.49). The association with cigarette smoking was stronger for mucinous tumors (OR = 1.77; 95% CI = 1.06-2.96) than for nonmucinous tumors (OR = 1.13; 95% CI = 0.89-1.44). In addition, the odds of smokers having mucinous tumors increased with years of smoking (OR = 1.36, 1.88, 1.19, 4.89 for <20, 21-30, 31-40 and >40 years, respectively; p for trend = 0.002), number of cigarettes smoked per day (OR = 1.55, 1.89, 2.28 for <10, 11-20 and >20 cigarettes/day, respectively; p for trend = 0.014) and smoking pack-years (OR = 1.13, 2.65, 1.77 and 2.39 for <10, 11-20, 21-30 and >30 pack-years, respectively; p for trend = 0.004). Our data suggest that cigarette smoking is associated with an increased risk of ovarian cancer, especially for mucinous types.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".