Theoretical potential for endometrial cancer prevention through primary risk factor modification: Estimates from the EPIC cohort
Bibliographic record
Abstract
Endometrial cancer (EC) incidence rates vary ~10‐fold worldwide, in part due to variation in EC risk factor profiles. Using an EC risk model previously developed in the European EPIC cohort, we evaluated the prevention potential of modified EC risk factor patterns and whether differences in EC incidence between a European population and low‐risk countries can be explained by differences in these patterns. Predicted EC incidence rates were estimated over 10 years of follow‐up for the cohort before and after modifying risk factor profiles. Risk factors considered were: body mass index (BMI, kg/m2), use of postmenopausal hormone therapy (HT) and oral contraceptives (OC) (potentially modifiable); and, parity, ages at first birth, menarche and menopause (environmentally conditioned, but not readily modifiable). Modeled alterations in BMI (to all ≤23 kg/m2) and HT use (to all non‐HT users) profiles resulted in a 30% reduction in predicted EC incidence rates; individually, longer duration of OC use (to all ≥10 years) resulted in a 42.5% reduction. Modeled changes in not readily modifiable exposures (i.e., those not contributing to prevention potential) resulted in ≤24.6% reduction in predicted EC incidence. Women in the lowest decile of a risk score based on the evaluated exposures had risk similar to a low risk countries; however, this was driven by relatively long use of OCs (median = 23 years). Our findings support avoidance of overweight BMI and of HT use as prevention strategies for EC in a European population; OC use must be considered in the context of benefits and risks.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".