Reproducibility of Biopsy Diagnoses of Endometrial Hyperplasia: Evidence Supporting a Simplified Classification
Bibliographic record
Abstract
BACKGROUND: Identifying which categories in the World Health Organization classification of endometrial hyperplasia contribute to suboptimal reproducibility is clinically important. METHODS: A 2-member panel reviewed 209 endometrial biopsy/curettage specimens originally diagnosed as incident endometrial hyperplasia as part of a progression study. Original diagnoses included the following: disordered proliferative endometrium, simple hyperplasia, complex hyperplasia, and atypical hyperplasia; panel diagnoses also included negative and carcinoma. We assessed percentage agreement and kappa statistics+/-standard errors (K+/-SE). RESULTS: Original and panel diagnoses (combining negative with disordered proliferative endometrium; atypical hyperplasia with carcinoma) agreed for 34.9% of biopsies (K-unweighted+/-SE=0.18+/-0.03; K-weighted+/-SE=0.27+/-0.04). Panelists' diagnoses agreed (using 6 categories) for 51.7% of biopsies, corresponding to K-unweighted+/-SE=0.37+/-0.03, improving with weighting to K-weighted+/-SE=0.63+/-0.05. Reproducibility based on a 2-tier classification ([negative, disordered proliferative endometrium, simple hyperplasia, or complex hyperplasia] versus [atypical hyperplasia or carcinoma]) increased agreement between original and panel diagnoses to 82.8%, K-unweighted+/-SE=0.37+/-0.06, and between panelists to 87.0%, K-unweighted+/-SE=0.63+/-0.07. Agreement between panelists at a cutpoint of complex hyperplasia and more severe versus simple hyperplasia or less severe was 88.0%, K-unweighted+/-SE=0.72+/-0.07. CONCLUSIONS: Developing and prospectively testing a binary system of classifying endometrial hyperplasia on endometrial biopsy may aid efforts to improve interobserver reproducibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".