Prevalence and predictors of atypical histology in endometrial polyps removed by hysteroscopy: A secondary analysis from the SICMIG hysteroscopy trial
Bibliographic record
Abstract
Background: The aim of this study is to assess the prevalence of atypical hyperplasia (AH) and endometrial cancer (EC) within endometrial polyps (EPs) removed by hysteroscopy.Methods: Hysteroscopic polypectomy interventions were performed over 1436 consecutive patients with Eps to complete a prospective observational trial (Canadian Task Force Classification II-2) including 19 Italian Gynecologic Departments (University-Affiliated or Public Hospitals) for a secondary multicenter analysis.Results: At histological analysis, in 1404 patients (97.8%) EPs were classified as benign, whereas in 32 patients (2.2%) EPs were diagnosed as atypical (i.e. with All or EC). Specifically, All and EC were found in 17 (1.2%) and 15 (1.0%) cases, respectively. Risk factor analysis showed that menopausal status, BMI and size of EPs were associated with increased risk of atypical EPs (p<0.0001). Abnormal uterine bleeding, EPs number, contraceptive therapy and tamoxifen were not associated with increased risk of atypia (p=ns). The cut-off points for increased risk of atypical polyps were 54.2 years old, BMI of 25.3 and EP size of 2.2 cm. Hysterectomy specimens were analyied in 21 women with atypical EPs, showing the concomitant presence of atypical tissue in non-polypoid endometrium in the majority of patients (n=14 women, 66.6%).Conclusion: The prevalence of endometrial cancer and atypical hyperplasia in endometrial polyps is low, although it is increased in women who are overweight, older than 54 years of age or with a polyp larger than 2cm.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".