Perinatal outcomes of midwife-led care, stratified by medical risk: a retrospective cohort study from British Columbia (2008–2018)
Bibliographic record
Abstract
BACKGROUND: Anecdotal evidence suggests that the profile of midwifery clients in British Columbia has changed over the past 20 years and that midwives are increasingly caring for clients with moderate to high medical risk. We sought to compare perinatal outcomes with a registered midwife as the most responsible provider (MRP) versus outcomes among clients with physicians as their MRP across medical risk strata. METHODS: = 425 056) and stratified the analysis by pregnancy risk status (low, moderate or high) according to an adapted perinatal risk scoring system. We estimated differences in outcomes between MRP groups by calculating adjusted absolute and relative risks. RESULTS: The adjusted absolute and relative risks of adverse neonatal outcomes were consistently lower among those who chose midwifery care across medical risk strata, compared with clients who had a physician as MRP. Midwifery clients experienced higher rates of spontaneous vaginal births, vaginal births after cesarean delivery and breastfeeding initiation, and lower rates of cesarean deliveries and instrumental births, with no increase in adverse neonatal outcomes. We observed an increased risk of oxytocin induction among high-risk birthers with a midwife versus an obstetrician as MRP. INTERPRETATION: Our findings suggest that compared with other providers in BC, midwives provide safe primary care for clients with varied levels of medical risk. Future research might examine how different practice and remuneration models affect clinical outcomes, client and provider experiences, and costs to the health care system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".