Endometrial cancer and meat consumption
Bibliographic record
Abstract
Diet plays an important role in the etiology of certain cancers, but there is limited evidence with regard to the association between diet and risk of endometrial cancer. Few prospective studies have investigated meat intake as a potential determinant of endometrial cancer risk. The objective of this study was to examine the association between endometrial cancer risk and total meat, red meat, processed meat, fish, and poultry intake. We conducted a case-cohort analysis within the Canadian Study of Diet, Lifestyle, and Health, a prospective cohort of 73 909 adults (39 614 women). Participants were recruited from 1992 to 1999, predominantly from three Canadian universities. We conducted a linkage with the Ontario Cancer Registry for the years 1992-2007 for the female cohort members, who resided in Ontario at the time of enrollment (n=26 024), to yield data on cancer incidence. The analytic sample was comprised of 107 incident cases and 1830 subcohort members, the latter being an age-stratified sample of the full cohort. A nonsignificant increase in the risk of endometrial cancer was associated with increased consumption of red meat [hazard ratio (HR)=1.62, 95% confidence intervals (CI)=0.86-3.08, for high vs. low intake; P trend=0.13)], processed meat (HR=1.45, 95% CI=0.80-2.61, for high vs. low intake; P trend=0.058), and all meat combined (HR=1.50, 95% CI=0.78-2.89, for high vs. low intake; P trend=0.14). No clear patterns were noted for poultry or fish. The results of this study, although based on a limited number of cases, suggest that relatively high meat intake may be associated with increased risk of endometrial cancer.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".