Ultrasound and menstrual history in predicting endometrial hyperplasia in polycystic ovary syndrome
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To assess the role of endometrial thickness on vaginal ultrasound assessment and menstrual history in predicting endometrial hyperplasia in women with polycystic ovary syndrome (PCOS) who presented with infertility due to anovulation. METHODS: This was a prospective study in a university referral-based fertility and endocrine clinic. Fifty-six women with PCOS presenting with infertility due to anovulation underwent both vaginal ultrasound assessments and endometrial biopsies. The main outcome measures were the predictive value of sonographic endometrial thickness (primary objective) and the menstrual history with other clinical characteristics (secondary objective) for proliferative endometrium and endometrial hyperplasia in logistic regression analysis. Their predictive value was further examined by receiver operating characteristic curve analysis. RESULTS: Thirty-six PCOS patients (64.3%) had proliferative endometrium and 20 (35.7%) had endometrial hyperplasia. Five of the latter (25%) had cytologic atypia. Endometrial thickness less than 7 mm or intermenstrual interval less than 3 months (corresponding to more than four menstrual periods yearly) was associated with proliferative endometrium only. The endometrial thickness correlated positively with endometrial hyperplasia (P =.018) and, together with the average intermenstrual interval, were significant predictors of endometrial hyperplasia (P <.001). CONCLUSION: These findings point to the usefulness of obtaining a detailed menstrual history in women with PCOS by identifying those at increased risk of endometrial hyperplasia and who require an endometrial biopsy. The endometrial thickness corroborates this clinical impression and is particularly useful when the menstrual history is uncertain. Endometrial hyperplasia in this population is effectively excluded when the endometrial thickness is less than 7 mm.
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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 | 0.000 | 0.010 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it