Dairy Products and Ovarian Cancer: A Pooled Analysis of 12 Cohort Studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Dairy foods and their constituents (lactose and calcium) have been hypothesized to promote ovarian carcinogenesis. Although case-control studies have reported conflicting results for dairy foods and lactose, several cohort studies have shown positive associations between skim milk, lactose, and ovarian cancer. METHODS: A pooled analysis of the primary data from 12 prospective cohort studies was conducted. The study population consisted of 553,217 women among whom 2,132 epithelial ovarian cases were identified. Study-specific relative risks and 95% confidence intervals were calculated by Cox proportional hazards models and then pooled by a random-effects model. RESULTS: No statistically significant associations were observed between intakes of milk, cheese, yogurt, ice cream, and dietary and total calcium intake and risk of ovarian cancer. Higher lactose intakes comparing > or = 30 versus <10 g/d were associated with a statistically significant higher risk of ovarian cancer, although the trend was not statistically significant (pooled multivariate relative risk, 1.19; 95% confidence interval, 1.01-1.40; P(trend) = 0.19). Associations for endometrioid, mucinous, and serous ovarian cancer were similar to the overall findings. DISCUSSION: Overall, no associations were observed for intakes of specific dairy foods or calcium and ovarian cancer risk. A modest elevation in the risk of ovarian cancer was seen for lactose intake at the level that was equivalent to three or more servings of milk per day. Because a new dietary guideline recommends two to three servings of dairy products per day, the relation between dairy product consumption and ovarian cancer risk at these consumption levels deserves further examination.
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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 it