Dairy Products and Ovarian Cancer: A Pooled Analysis of 12 Cohort Studies
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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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.022 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".