Processes of obstetrical care and outcomes among Ontario physicians versus non-physicians: a population-based study
Bibliographic record
Abstract
OBJECTIVE: We compared processes of antepartum, intrapartum and postpartum care and obstetrical outcomes between physicians and non-physicians. DESIGN: This is a population-based retrospective matched cohort study. SETTING: The study was conducted in Ontario, Canada. PARTICIPANTS: Physicians and non-physicians residing in high-income urban areas from 1 April 2009 to 26 November 2018 were included. Physicians were matched to non-physicians on maternal age, calendar year, parity, conception by assisted reproductive technology and singleton versus multifetal gestation. We compared processes of antepartum, intrapartum and postpartum care between physicians and non-physicians. OUTCOME MEASURES: The primary outcome was mode of delivery (caesarean section, C-section vs vaginal delivery). Secondary outcomes included obstetrical anal sphincter injury among those experiencing vaginal birth and differences in urgent healthcare contacts (maternal and neonatal) during the postpartum period. RESULTS: 7327 physicians were matched 1:5 to 36 185 non-physicians and were well balanced except for comorbidities (physicians had fewer comorbidities). Physicians had more antenatal ultrasounds and invasive prenatal testing, received labour anaesthesia more often and were more often delivered by their own care provider. In adjusted analyses, physicians and non-physicians had a similar risk of C-section (aRR 0.97, 95% CI 0.93 to 1.00, p=0.07). There was no difference in neonatal urgent care contacts; non-physicians had a higher risk of maternal urgent postpartum care (adjusted relative risk [aRR] 1.22, 95% CI 1.08 to 1.37, p<0.0001). CONCLUSIONS: Physicians and non-physicians of similar age and with similar pregnancy characteristics had a comparable rate of C-section, which may be related to a lack of cost drivers for C-section in Ontario.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".