Likelihood Ratio of Sonohysterographic Findings for Discriminating Endometrial Polyps From Submucosal Fibroids
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to determine which combination of sonohysterographic features has the highest likelihood ratios (LRs) in discriminating polyps from submucosal fibroids. METHODS: This retrospective study included 200 consecutive patients who underwent both sonohysterography and a procedure resulting in a positive pathologic diagnosis. A reader, masked to the imaging and pathologic findings, independently reviewed the 200 sonograms and recorded the findings using a standardized checklist for sonographic features on sonohysterography. The features assessed included angle, echogenicity, endometrial-myometrial interface, and vascular pattern, among others. The reader chose one final diagnosis from the list of possibilities, which included normal, hyperplasia, polyp, submucosal fibroid, cancer, adhesions, and clots. Sonographic observations were then compared to pathologic findings. RESULTS: The LR of 13.4 was achieved for polyps when there was a combination of an intact endometrial-myometrial interface, a single vessel, an acute angle, and homogeneous echogenicity. The highest LR of 27.8 was achieved for submucosal fibroids when the combination of sonographic features included an absent endometrial-myometrial interface, an arborized/multiple vascular pattern, an obtuse angle, and heterogeneous echogenicity. CONCLUSIONS: A combination of sonographic findings may provide high LRs for discriminating endometrial polyps from submucosal fibroids.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".