Uterine artery embolization for uterine arteriovenous malformation in five women desiring fertility: pregnancy outcomes
Bibliographic record
Abstract
Uterine arteriovenous malformations (AVM) are rare and can be classified as either congenital or acquired. Acquired AVMs may result from trauma, uterine instrumentation, infection or gestational trophoblastic disease. The majority of acquired AVMs are encountered in women of reproductive age with a history of at least one pregnancy. Traditional therapies of AVMs include medical management of symptomatic bleeding, blood transfusions, uterine artery embolization (UAE) or hysterectomy. In this retrospective case series, we report our experience with AVM and UAE in five symptomatic women of reproductive age who wished to preserve fertility. Patients were 18-32 years old, and had 1-3 previous pregnancies prior to initial presentation. All patients were followed until their deliveries. All five patients delivered live births. Three of the five patients required two embolization procedures and one of these women required a subsequent hysterectomy. Two deliveries were at term and had normal weight babies and normal placenta. One woman had cerclage placed and developed chorioamnionitis at 34 weeks but had a normal placenta. Two pregnancies were induced <37 weeks for pre-eclampsia/b intrauterine growth restriction ± abnormal umbilical artery dopplers. The low birthweight were both <2000 g. Both placentas showed accelerated maturity and infarcts. All estimated blood losses were recorded as <500 cc. In conclusion, UAE may not be as effective at managing AVM as previously thought and should be questioned as an initial therapy in symptomatic women of reproductive age desiring fertility preservation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".