Differences in Obstetric and Cardiac Outcomes of Pregnant CHD Patients by Rurality [ID 1628]
Bibliographic record
Abstract
INTRODUCTION: The objective was to examine the association between patient residence (urban or rural), and both major adverse cardiac events (MACEs) and adverse pregnancy outcomes (APOs) in pregnant women with congenital heart disease (CHD). METHODS: A retrospective cohort study from 2016–2021 HCUP-NIS (National Inpatient Sample) database was conducted using pregnant Americans with CHD and their location (urban or rural). HCUP-NIS represents 96% of the American population with over nine million admissions annually. The primary outcomes were MACEs and APOs. Multivariate logistic regression with survey procedures and weighted odds ratios were used to represent national estimates. RESULTS: The weighted sample represented 24,295 (n=4,859) patients, of which 20,840 (n=4,168) were in urban settings and 3,455 (n=691) in rural settings. Rurality was associated with higher odds of MACE with a trend towards significance (adjusted odds ratio [aOR] 1.18; 95% CI, 0.98–1.41; P=.08), and associated with lower odds of APO (aOR 0.76; 95% CI, 0.63–0.91; P=.003). Stratification by complexity showed rural patients with moderate CHD to have highest odds of MACE (OR 1.92; 95% CI, 0.92–1.55; P=.19) and rural patients with severe CHD to have lowest odds of APO (OR 0.60; 95% CI, 0.33–1.09; P=.09). An unadjusted model with an interaction term showed no effect modification of patient rurality by complexity of MACE (P=.66) or APO (P=.60). CONCLUSIONS/IMPLICATIONS: Pregnant patients with CHD living in rural settings have an increased odds of MACE, with a trend towards significance, and decreased odds of APO, possibly due to referrals of highest risk patients to urban centers. These results support the need for multidisciplinary obstetrical programs for CHD patients in rural settings, either in person or through telehealth.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".