Pregnancy, delivery, and neonatal outcomes among women living with Down syndrome: a matched cohort study, utilizing a population database
Bibliographic record
Abstract
PURPOSE: We utilized a population database to address the paucity of data around pregnancy outcomes in women with Down syndrome (DS). METHODS: We conducted a retrospective study using the Health Care Cost and Utilization Project-Nationwide Inpatient Sample Database over 11 years, from 2004 to 2014. 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, race, income, and health insurance type to women without DS (control) at a ratio of 1:20. RESULTS: There were a total of 9,096,788 deliveries during the study period. Of those, 184 pregnant women were found to have DS. The matched control group was 3680. After matching, most of the pregnancy and delivery outcomes, such as pregnancy-induced hypertension, gestational diabetes, preterm premature rupture of membrane, chorioamnionitis, cesarean section, operative vaginal delivery, or blood transfusion were similar between participants with and without DS. However, patients with DS were at increased risk of giving birth prematurely (aOR 3.09, 95% CI 2.06-4.62), and having adverse neonatal outcomes such as small for gestational age (aOR 2.70, 95% CI 1.54-4.73), intrauterine fetal demise (aOR 22.45, 95% CI 12.02-41.93), congenital anomalies (aOR 7.92, 95% CI 4.11-15.24), and fetal chromosomal abnormalities. CONCLUSION: Neonates to mothers with DS are at increased risk of prematurity and other neonatal adverse outcomes. Hence, counseling patients with DS about these risks and increased antenatal surveillance is advised.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".