How do bicornuate uteri alter pregnancy, intra-partum and neonatal risks? A population based study of more than three million deliveries and more than 6000 bicornuate uteri
Bibliographic record
Abstract
OBJECTIVES: To explore maternal and neonatal outcomes in pregnant women with bicornuate uteri. METHODS: Retrospective population-based cohort study utilizing data from the Healthcare-Cost and Utilization Project-Nationwide Inpatient Sample (HCUP-NIS) from 2010 to 2014. There were 3,846,342 births between 2010 and 2014, included in the study. Six thousand and 195 deliveries were to women with bicornuate uterus. The remaining deliveries without other uterine anomalies were categorized as the reference group (n=3,840,147). RESULTS: Pregnant women with bicornuate uterus were older and more likely to be obese (p=0.0001) with previous cesarean deliveries (CD) (31 vs. 17.1%, p=0.0001). After adjustment for confounders, they were more likely to experience pregnancy-induced hypertension (HTN) (aOR 1.21, 95%CI: 1.1-1.3), p=0.0001), preeclampsia (aOR 1.4, 95%CI: 1.2-1.6, p=0.0001) and placenta previa (aOR 1.7, 95%CI: 1.3-2.2, p=0.0001). Moreover, they were more likely to deliver preterm (aOR 2.8, 95%CI: 2.6-3.1, p=0.0001), deliver by CD (aOR 5, 95%CI: 3.1-4.1, p=0.0001), experience preterm pre-labor rupture of membranes (PPROM) (aOR 3.5, 95%CI: 2.6-3.1, p=0.0001), and have a placental abruption (aOR 3.0, 95%CI: 2.5-3.5, p=0.0001). There were increased risks of PPH (aOR 1.4, 95%CI: 1.2-1.6, p=0.0001), wound-complications (aOR 2.0, 95%CI: 1.5-2.7, p=0.0001), hysterectomy (aOR 2.6, 95%CI: 1.6-4.1, p=0.0001), blood-transfusion (aOR 1.7, 95%CI: 1.5-2.1, p=0.0001), and DIC (aOR 1.6, 95%CI: 1.1-2.5), p=0.014) in the group with bicornuate uteri. Also there was higher risk of SGA (aOR 2.9, 95%CI: 2.6-3.2, p=0.0001) and IUFD (aOR 2.5, 95%CI: 1.8-3.3, p=0.0001). CONCLUSIONS: Bicornuate uteri can increase risks in pregnancy by many folds. Particularly risks of: premature delivery, CD, PPROM, placental abruption, hysterectomy, SGA and IUFD were increased 250-500%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".