The etiology of uterine sarcomas: a pooled analysis of the epidemiology of endometrial cancer consortium
Bibliographic record
Abstract
BACKGROUND: Uterine sarcomas are characterised by early age at diagnosis, poor prognosis, and higher incidence among Black compared with White women, but their aetiology is poorly understood. Therefore, we performed a pooled analysis of data collected in the Epidemiology of Endometrial Cancer Consortium. We also examined risk factor associations for malignant mixed mullerian tumours (MMMTs) and endometrioid endometrial carcinomas (EECs) for comparison purposes. METHODS: We pooled data on 229 uterine sarcomas, 244 MMMTs, 7623 EEC cases, and 28,829 controls. Odds ratios (ORs) and 95% confidence intervals (CIs) for risk factors associated with uterine sarcoma, MMMT, and EEC were estimated with polytomous logistic regression. We also examined associations between epidemiological factors and histological subtypes of uterine sarcoma. RESULTS: Significant risk factors for uterine sarcoma included obesity (body mass index (BMI)≥30 vs BMI<25 kg m(-2) (OR: 1.73, 95% CI: 1.22-2.46), P-trend=0.008) and history of diabetes (OR: 2.33, 95% CI: 1.41-3.83). Older age at menarche was inversely associated with uterine sarcoma risk (≥15 years vs <11 years (OR: 0.70, 95% CI: 0.34-1.44), P-trend: 0.04). BMI was significantly, but less strongly related to uterine sarcomas compared with EECs (OR: 3.03, 95% CI: 2.82-3.26) or MMMTs (OR: 2.25, 95% CI: 1.60-3.15, P-heterogeneity=0.01). CONCLUSION: In the largest aetiological study of uterine sarcomas, associations between menstrual, hormonal, and anthropometric risk factors and uterine sarcoma were similar to those identified for EEC. Further exploration of factors that might explain patterns of age- and race-specific incidence rates for uterine sarcoma are needed.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 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".