Effect of Uterine Cavity Sonographic Measurements on Medical Management Failure in Women With Early Pregnancy Loss
Bibliographic record
Abstract
OBJECTIVES: Medical management is commonly used among women with early pregnancy failure. The purpose of our study was to evaluate uterine content sonographic measurements for predicting medical management failure in early pregnancy loss. METHODS: We conducted a retrospective cohort study in a university-affiliated hospital center including all women discharged from the emergency department (ED) with a diagnosis of early pregnancy failure who had medical management with misoprostol between 2011 and 2013. Only women with sonograms available for review were included in our study. All images were reviewed and the following cavity measurements, excluding the endometrial lining, were measured: cavity anteroposterior distance, cavity longitudinal distance, cavity transverse distance, and cavity volume. Logistic regression analysis was used to identify measurements that were independently associated with a subsequent need for dilation and curettage (D&C) and an unplanned return to the ED. RESULTS: Among 823 women presenting to the ED with first-trimester bleeding, 227 met inclusion criteria. Of all measurements evaluated, the cavity anteroposterior distance was found to be independently associated with D&C and an unplanned return to the ED. When a cavity anteroposterior distance cutoff of 15 mm was used, women were more likely to require D&C (adjusted odds ratio, 2.65; 95% confidence interval, 1.31-5.36; P< .01) and to have an unplanned return to the ED (adjusted odds ratio, 2.59; 95% confidence interval, 1.41-4.79; P < .01). In women with a cavity anteroposterior distance of less than 15 mm, 87.1% had successful medical management of early pregnancy loss, and 80.0% did not require an unplanned return to the ED. CONCLUSIONS: Although there is a need for further validation, patients identified as having a cavity anteroposterior distance of less than 15 mm should be considered good candidates for successful medical management.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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 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".