Rural community birth: Maternal and neonatal outcomes for planned community births among rural women in the United States, 2004‐2009
Bibliographic record
Abstract
BACKGROUND: Approximately 22% of women in the United States live in rural areas with limited access to obstetric care. Despite declines in hospital-based obstetric services in many rural communities, midwifery care at home and in free standing birth centers is available in many rural communities. This study examines maternal and neonatal outcomes among planned home and birth center births attended by midwives, comparing outcomes for rural and nonrural women. METHODS: Using the Midwives Alliance of North America Statistics Project 2.0 dataset of 18 723 low-risk, planned home, and birth center births, rural women (n = 3737) were compared to nonrural women. Maternal outcomes included mode of delivery (cesarean and instrumental delivery), blood transfusions, severe events, perineal lacerations, or transfer to hospital and a composite (any of the above). The primary neonatal outcome was a composite of early neonatal intensive care unit or hospital admissions (longer than 1 day), and intrapartum or neonatal deaths. Analysis involved multivariable logistic regression, controlling for sociodemographics, antepartum, and intrapartum risk factors. RESULTS: Rural women had different risk profiles relative to nonrural women and reduced risk of adverse maternal and neonatal outcomes in bivariable analyses. However, after adjusting for risk factors and confounders, there were no significant differences for a composite of maternal (adjusted odds ratio [aOR] 1.05 [95% confidence interval {CI} 0.93-1.19]) or neonatal (aOR 1.13 [95% CI 0.87-1.46]) outcomes between rural and nonrural pregnancies. CONCLUSION: Among this sample of low-risk women who planned midwife-led community births, no increased risk was detected by rural vs nonrural status.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".