Intake of Coffee and Tea and Risk of Ovarian Cancer: A Prospective Cohort Study
Bibliographic record
Abstract
There is some evidence from case-control studies that coffee consumption might be positively associated with ovarian cancer risk, whereas the epidemiologic evidence regarding tea consumption and ovarian cancer is inconsistent. To date, there have been few prospective studies of these associations. Therefore, we examined ovarian cancer risk in association with both coffee and tea intake in a prospective cohort study of 49,613 Canadian women enrolled in the National Breast Screening Study (NBSS) who completed a self-administered food frequency questionnaire between 1980 and 1985. Linkages to national mortality and cancer databases yielded data on deaths and cancer incidence, with follow-up ending between 1998 and 2000. Data from the food frequency questionnaire were used to estimate daily intake of coffee and tea. Cox proportional hazards models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association between categories of coffee and tea intake and ovarian cancer risk. During a mean 16.4 years of follow-up, we observed 264 incident ovarian cancer cases. Tea intake was not associated with ovarian cancer risk in our study population. In contrast, a borderline positive association was observed among women who drank > 4 cups coffee/day compared to women who did not drink coffee (HR = 1.62, 95% CI = 0.95-2.75, P(trend) = 0.06). Given the pervasive use of these beverages, the associations between coffee and tea consumption and ovarian cancer risk warrant investigation in further prospective studies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".