A Case-Control Study of Diet and the Risk of Ovarian Cancer
Bibliographic record
Abstract
Epidemiologic studies have suggested that some dietary factors may play a role in the etiology of ovarian cancer, but the findings have been inconsistent. We assessed the association of ovarian cancer with dietary factors in a population-based case-control study in Canada. Diet information was collected on 442 incident cases of ovarian cancer diagnosed in 1994 to 1997 and 2,135 population controls via a self-administered questionnaire. Compared with women in the lowest quartile of cholesterol intake, those in the second, third, and fourth quartiles had a multivariate adjusted odds ratio [OR; 95% confidence interval (95% CI)] of 1.12 (0.81-1.56), 1.20 (0.85-1.68), and 1.42 (1.03-1.97), respectively (P for trend = 0.031). Higher egg consumption was also associated with a nonsignificant increase in ovarian cancer risk. The ORs (95% CIs) for ovarian cancer were 0.77 (0.60-1.04) and 0.76 (0.56-0.99) among women in the highest quartile of total vegetable and cruciferous vegetable intake as compared with women in the lowest quartile. Women who took supplements of vitamin E, beta-carotene, and B-complex vitamins for > or =10 years had ORs (95% CIs) of 0.49 (0.30-0.81), 0.31 (0.11-0.91), and 0.61 (0.36-1.05), respectively. However, we did not observe an association of ovarian cancer risk with dietary fat intake, including saturated, monounsaturated, and polyunsaturated fatty acids, protein, carbohydrate, dietary fiber, fruit, dairy products, meat products, fish, chicken, grain products, nut products, baked desserts, margarine, butter, mayonnaise, and supplement of multiple vitamins, vitamin A, vitamin C, calcium, iron, zinc, and selenium. Our findings suggested that ovarian cancer risk was positively associated with higher consumption of dietary cholesterol and eggs and inversely associated with higher intake of total vegetables and cruciferous vegetables and supplementation of vitamin E, beta-carotene, and B-complex vitamins.
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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 | 0.002 | 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".