Ultrasound in gynecology P133The endometrium after 50: sonographic‐histologic correlation
Bibliographic record
Abstract
We wanted to test the accuracy of sonography in detecting endometrial pathology, in women over 50. Retrospectively, we enrolled 100 consecutive women over 50‐year‐old, who have had a monographic endometrial thickness measurement followed by a histological endometrial analysis, though endometrial biopsy, dilatation and curetage, or hysterectomy. The mean age was 64.3 years; 54% of the women presented metrorragies and 40% of them did not use hormonal replacement therapy (HRT). We found 6 endometrial cancers and 2 atypical hyperplasia (all of them were symptomatic); we found 7 simple hyperplasia (2 were asymptomatic) and 18 polyps (6 were asymptomatic). No serious endometrial anomaly (simple or atypical hyperplasia or adenocarcinoma) were found under 5 mm of double endometrial layer. The first endometrial cancer was encountered in an endometrial thickness of 10 mm, the first atypical hyperplasia at 9 mm, the first simple hyperplasia at 7 mm, and the first polyp in a 2‐mm endometrial thickness. If we used the endometrial thickness cut‐off level of < 5 mm for symptomatic women and for asymptomatic women without hormonal replacement therapy and the endometrial thickness cut‐off level of < 8 mm for asymptomatic women with HRT, we obtain a sensitivity in detecting any endometrial pathology of 85%, and a specificity of 41%; the false negative rate is low (5%) and represent in our study only small polyps.
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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 | 0.001 | 0.005 |
| 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) | 0.003 | 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".