Uterine Prolapse in Pregnancy: National Trends, Risk Factors, and Obstetric Outcomes [A141]
Bibliographic record
Abstract
INTRODUCTION: The uterus can prolapse acutely during pregnancy. We report national trends, characteristics, and obstetric outcomes of women with uterine prolapse (UP). METHODS: Retrospective cohort study using the HCUP-NIS database. Our study cohort included women admitted for delivery between 2004 and 2014 inclusively and had a diagnosis of UP. Multivariate logistic regression analyses compared obstetric outcomes among pregnancies complicated by UP versus those without, while adjusting for confounding variables. RESULTS: Among the 9,096,788 deliveries during our study period, 713 received a diagnosis of UP. The incidence of UP increased over time (P≤0.0001). Women with UP were likelier to be older, multigravida, smokers, have endometriosis, or fibroids (P≤0.002, all). They were less likely to have had a previous cesarean delivery (CD) (P<.0001), or multiple gestation (P=.03). Women with UP had lower odds of pregnancy-induced hypertension (aOR, 0.38; 95% CI, 0.25–0.58), preeclampsia (aOR, 0.27; 95% CI, 0.13–0.57), operative vaginal delivery (aOR, 0.41; 95% CI, 0.26–0.64), and CD (aOR, 0.30; 95% CI, 0.23–0.39). They had higher odds of having a spontaneous vaginal delivery (aOR, 3.46; 95% CI, 2.73–4.39), para-delivery hysterectomy (aOR, 21.5; 95% CI, 11.78–29.25), postpartum hemorrhage (aOR, 2.12; 95% CI, 1.56–2.87), wound complications (aOR, 2.72; 95% CI, 1.22–6.09), blood transfusion (aOR, 2.39; 95% CI, 1.43–3.10) or birth of neonates with major congenital abnormalities (aOR, 2.48; 95% CI, 1.18–5.22). CONCLUSION: Women with UP are at higher risk of obstetric complications and neonatal morbidity. These findings elucidate risk factors that may improve prenatal counseling, perinatal surgical management, and clinical practice and policy guidelines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".