Understanding risk factors for endometrial cancer in young women
Bibliographic record
Abstract
BACKGROUND: The American Cancer Society recommends physicians inform average-risk women about endometrial cancer risk on reaching menopause, but new diagnoses are rising fastest in women aged younger than 50 years. Educating these younger women about endometrial cancer risks requires knowledge of risk factors. However, endometrial cancer in young women is rare and challenging to study in single study populations. METHODS: We included 13 846 incident endometrial cancer patients (1639 aged younger than 50 years) and 30 569 matched control individuals from the Epidemiology of Endometrial Cancer Consortium. We used generalized linear models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for 6 risk factors and endometrial cancer risk. We created a risk score to evaluate the combined associations and population attributable fractions for these factors. RESULTS: In younger and older women, we observed positive associations with body mass index and diabetes and inverse associations with age at menarche, oral contraceptive use, and parity. Current smoking was associated with reduced risk only in women aged 50 years and older (Phet < .01). Body mass index was the strongest risk factor (OR≥35 vs<25 kg/m2 = 5.57, 95% CI = 4.33 to 7.16, for ages younger than 50 years; OR≥35 vs<25 kg/m2 = 4.68, 95% CI = 4.30 to 5.09, for ages 50 years and older; Phet = .14). Possessing at least 4 risk factors was associated with approximately ninefold increased risk in women aged younger than 50 years and approximately fourfold increased risk in women aged 50 years and older (Phet < .01). Together, 59.1% of endometrial cancer in women aged younger than 50 years and 55.6% in women aged 50 years and older were attributable to these factors. CONCLUSIONS: Our data confirm younger and older women share common endometrial cancer risk factors. Early educational efforts centered on these factors may help mitigate the rising endometrial cancer burden in young women.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".