Fruits and Vegetables and Ovarian Cancer Risk in a Pooled Analysis of 12 Cohort Studies
Bibliographic record
Abstract
Because fruits and vegetables are rich in bioactive compounds with potential cancer-preventive actions, increased consumption may reduce the risk of ovarian cancer. Evidence on the association between fruit and vegetable intake and ovarian cancer risk has not been consistent. We analyzed and pooled the primary data from 12 prospective studies in North America and Europe. Fruit and vegetable intake was measured at baseline in each study using a validated food-frequency questionnaire. To summarize the association between fruit and vegetable intake and ovarian cancer, study-specific relative risks (RR) were estimated using the Cox proportional hazards model, and then combined using a random-effects model. Among 560,441 women, 2,130 cases of invasive epithelial ovarian cancer occurred during a maximum follow-up of 7 to 22 years across studies. Total fruit intake was not associated with ovarian cancer risk-the pooled multivariate RR for the highest versus the lowest quartile of intake was 1.06 [95% confidence interval (95% CI), 0.92-1.21; P value, test for trend = 0.73; P value, test for between-studies heterogeneity = 0.74]. Similarly, results for total vegetable intake indicated no significant association (pooled multivariate RR, 0.90; 95% CI, 0.78-1.04, for the highest versus the lowest quartile; P value, test for trend = 0.06; P value, test for between-studies heterogeneity = 0.31). Intakes of botanically defined fruit and vegetable groups and individual fruits and vegetables were also not associated with ovarian cancer risk. Associations for total fruits and vegetables were similar for different histologic types. These results suggest that fruit and vegetable consumption in adulthood has no important association with the risk of ovarian cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".