Survey on Reporting of Endometrial Biopsies From Women on Progestogen Therapy for Endometrial Atypical Hyperplasia/Endometrioid Carcinoma
Bibliographic record
Abstract
Histologic assessment of response to progestogen therapy is a cornerstone of nonsurgical management of atypical hyperplasia/low-grade endometrioid carcinoma. Pathologists are required to assess whether there is ongoing preneoplastic or neoplastic change in the biopsies (often multiple) taken during therapy. There have been few studies documenting the specific histologic changes induced by therapeutic progestogens and currently there are no guidelines on terminology used in this scenario. Given the need for uniformity in reporting and the lack of guidance in the current literature, we initiated an online survey (including questions, categories of reporting, and scanned slides for assessment) which was sent to all members of British Association of Gynaecological Pathologists (BAGP) and the International Society of Gynecological Pathologists (ISGyP) with the aim to assess the variability among pathologists in reporting these specimens and to come up with a consensus-based terminology for reporting of endometrial biopsies from women on progestogen therapy for endometrial atypical hyperplasia/endometrioid carcinoma. In total, 95 pathologists participated in this survey. This manuscript elaborates on the results of the survey with recommendations aimed at promoting uniform terminology in reporting these biopsies.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".