What types of uterine anomalies, if any, cause more pregnancy complications, compared to the other anomalies? An evaluation of a large population database.
Bibliographic record
Abstract
Objective: to compare pregnancy risks between different congenital uterine anomalies utilizing other congenital anomalies as a control group in a large population database. Design, setting, and sample: A retrospective population-based cohort study from the Healthcare-Cost-Utilization Project-Nationwide-Inpatient-Sample(HCUP-NIS) included-3,846,342 births(2010-2014). Of them 6195 deliveries were to women with bicornuate uteri, 798 with arcuate uteri, 2255 with didelphys uteri, 802 with unicornuate uteri and 1404 with septate uteri. Main Outcome Measures and Results: After adjustent for confounders, women with bicornuate uteri were more likely to deliver vaginally(aOR 1.4, 95%CI: 1.1-1.9), P=0.01), less likely to deliver by cesarean(CD) and had lower risk of SGA(aOR 0.8, 95%CI: 0.7-0.9, P=0.03) when compared to the other anomalies (aOR 0.6, 95%CI: 0.5-0.6), P=0.0001). Pregnant women with arcuate uterus had lower risks of preterm delivery((aOR 0.6, 95%CI: 0.5-0.8), P=0.0001), less chance of operative vaginal delivery(aOR 0.5, 95%CI: 0.2-0.9), P=0.04), and higher risk for CD(aOR 1.6, 95%CI: 1.4-2, P=0.0001). Pregnant women with didelphys uteri had higher risk of PPROM(aOR 1.6, 95%CI: 1.3-1.9), P=0.0001), preterm delivery(aOR 1.5, 95%CI: 1.3-1.6), P=0.0001), CD(aOR 1.4, 95%CI: 1.2-1.5, P=0.0001) and wound complications (aOR 1.6, 95%CI: 1.1-2.4), P=0.02). Pregnant unicornuate uteri had increased risks of preterm delivery(aOR 1.4, 95%CI: 1.1-1.6), P=0.0001), CD(aOR 2, 95%CI: 1.6-2.5), P=0.0001) and of SGA(aOR 1.8, 95%CI: 1.4-2.3, P=0.0001). Pregnant septate uteri had higher risk of chorioamnionitis(aOR 1.5, 95%CI: 1.1-2.1), P=0.048) and CD(aOR 1.4, 95%CI: 1.2-1.6), P=0.0001). Conclusion: We demonstrated that there are different risks for certain adverse pregnancy and neonatal outcomes in diverse uterine anomalies as compared to the other anomalies
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".