Pregnancy, delivery and neonatal outcomes among women living with Down syndrome. A matched cohort study, taken from a population database.
Bibliographic record
Abstract
Objectives: Women with Down syndrome (DS) suffer from several health issues, however, their fecundity is not affected. Despite that, there are no studies in the literature to address pregnancy, delivery, or neonatal outcomes among women with DS. Design: We conducted a retrospective study using the Health Care Cost and Utilization Project-Nationwide Inpatient Sample Database over 11 years from 2004 to 2014. Methods: A delivery cohort was created using ICD-9 codes. ICD-9 code 758.0 was used to extract the cases of maternal DS. Pregnant women with DS (study group) were matched based on age and health insurance type to women without DS (control) at a ratio of 1:4. A multivariant logistic regression model was used to adjust for statistically significant variables (P-value < 0.5). Results: There were a total of 9,096,788 deliveries during the study period. Of those, 185 pregnant women were found to have DS. The matched control group was 740. Maternal pregnancy risks mostly did not differ between those with and without DS including pregnancy-induced PIH, gestational diabetes, preeclampsia, PPROM, chorioamnionitis, cesarean section, operative vaginal delivery, or blood transfusion (P >0.05, all). However, they were at extremely increased risk of delivering prematurely (aOR 3.86, 95% CI 1.25-11.93), and to have adverse neonatal outcomes such as small for gestational age (aOR 13.13, 95% CI 2.20-78.41), intrauterine fetal demise (aOR 20.97, 95% CI 1.86-237.02), and congenital anomalies (aOR 9.59, 95% CI 1.47-62.72). Conclusion: Women with DS should be counseled about their increased risk of premature delivery and adverse neonatal outcomes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".