Effect of implementation of the MOREOB program on adverse maternal and neonatal birth outcomes in Ontario, Canada: a retrospective cohort study
Bibliographic record
Abstract
In 2002, the MOREOB (Managing Obstetrical Risk Efficiently) obstetrical patient safety program was phased-in across hospitals in Ontario, Canada. The purpose of our study was to evaluate the effect of the MOREOB program on rates of adverse maternal and neonatal outcomes. A retrospective cohort study, using province-wide administrative hospitalization data. We included maternal and neonatal records between fiscal years 2002–2003 and 2013–2014, for deliveries taking place at the 67 Ontario hospitals where the MOREOB program was implemented between 2002 and 2012. After accounting for institutional mergers and excluding very small hospitals, 55 hospitals (1,447,073 deliveries) were included. Multivariable logistic and linear mixed effects regression analysis were used, accounting for secular trends, within hospital correlation and over time correlation, and adjusting for a maternal comorbidity index, hospital annual birth volume, and level of care. The main outcome measure was a composite individual-level indicator of incidence of any adverse events, and a hospital-level score, called the Weighted Adverse Outcome Score (WAOS) capturing both maternal and neonatal adverse outcomes. Across the 12 years of follow up, there were 98,789 adverse maternal and neonatal outcomes, a rate of 6.83 per 100 deliveries (6.66 per 100 occurring before, 6.91 per 100 during, and 6.84 per 100 after program implementation). The multivariable analysis found no statistically significant decrease in adverse events associated with program implementation (OR for adverse events after versus before =1.11 (95% CI: 1.06 to 1.17, change in mean WAOS score after minus before =0.15 (− 0.36 to 0.67)). We did not find a reduction in the incidence of maternal and neonatal adverse outcomes associated with the MOREOB program, and small yet statistically significant increases in some adverse events were observed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".