Case-Control Study of Dietary Patterns and Endometrial Cancer Risk
Bibliographic record
Abstract
Dietary patterns, rather than intakes of specific foods or nutrients, may influence risk of endometrial cancer (EC). This population-based case-control study in Canada (2002-2006) included incident EC cases (n = 506) from the Alberta Cancer Registry and controls frequency age-matched to cases (n = 981). Past-year dietary patterns were defined using factor analysis of food frequency questionnaire data. Logistic regression was used to estimate EC risk within quartiles of dietary patterns. Three patterns (sweets, meat, plants) explained 23% of the variance in the dietary data. In multivariable models, EC risk was significantly reduced by 30% for women in the highest quartile of the healthier plants pattern (OR = 0.70, 95% CI 0.50-0.98, P trend = 0.02). When stratified by body mass index (BMI; kg/m(2)), risk was further reduced among overweight or obese women with a BMI ≥25 (OR = 0.57, 95% CI 0.39-0.83; P trend = 0.004). EC was not associated with the less healthy sweets and meat patterns. However, risk was modestly, but not significantly, elevated for higher intakes of the meat pattern among overweight or obese women. A mostly plant-based dietary pattern may reduce EC risk. Recommendations for risk reduction should focus on maintaining a healthy weight and the role of diet should be studied further.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".