Effect of Menstrual Age on Medical Management Failure in Women With Early Pregnancy Loss [295]
Bibliographic record
Abstract
INTRODUCTION: Women with gestational age less than 8 weeks are considered good candidates for medical management of early pregnancy failure. The purpose of our study was to evaluate the effect of menstrual age on failed medical management among women with early pregnancy failure estimated at less than 8 weeks of gestation by ultrasonography. METHODS: We conducted a retrospective cohort study on all women discharged from the emergency department with a diagnosis of early pregnancy failure who were managed with misoprostol and had a gestational age less than 8 weeks on ultrasonography between 2011 and 2013. We used logistic regression to estimate the effect of menstrual age on failed medical management defined as dilatation and curettage (D&C) or unplanned return to the emergency department. RESULTS: Among 823 women presenting to the emergency department with first-trimester bleeding, 199 had failed pregnancy less than 8 weeks of gestation by ultrasonography and were discharged with misoprostol. Menstrual age was associated with an increased risk of D&C and unplanned return to the emergency department. Specifically, risk of D&C was 11.8% at less than 8 weeks of gestation, 18.5% at 8–9 weeks of gestation, 25.3% at 10–11 weeks of gestation, and 30.6% at 12 weeks of gestation or greater (P=.04). As well, risk of unplanned return to the emergency department was 14.7% at less than 8 weeks of gestation, 27.8% at 8–9 weeks of gestation, 36.0% at 10–11 weeks of gestation, and 41.7% at 12 weeks of gestation or greater (P=.01). CONCLUSION: Menstrual gestational age is an important predictor of failed medical management of early pregnancy loss that is independent of ultrasound-estimated gestational age. Menstrual gestational age should be considered when discussing treatment options with women who have an early pregnancy failure.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".