Feasibility of establishing a Canadian Obstetric Survey System (CanOSS) for severe maternal morbidity: results of a nationwide survey
Bibliographic record
Abstract
Objective: Obtaining data on events, processes, and circumstances leading to severe maternal morbidity (SMM) could enable targeted interventions to improve care. We aimed to assess the feasibility of gathering such data from across Canada through an Obstetric Survey System (CanOSS). Study design: A nationwide survey. Methods: We administered the electronic survey in French or English to birthing unit leads across all Canadian provinces and territories using REDCap. We presented pooled participation rates (95 % confidence intervals [CI]) across birthing units from lowest, medium, and highest tiers of service using Freeman-Tukey double arcsine transformations and common-effect models. Results: Of the 289 birthing units across Canada, 167 (57.8 %) participated in the survey. Pooled participation rates per province and territory stratified by highest, medium, and lowest tiers of service were 91.5 % (95 % CI [73.4, 100]), 58.6 % (95 % CI [48.5, 68.6]), and 54.4 % (95 % CI [41.7, 66.3]), respectively. Units reported postpartum hemorrhage (82.5 %), hypertensive disorders (65.7 %), infections (35.0 %), venous thromboembolism (16.0 %), and maternal birth injuries (15.4 %) as the leading causes of SMM. Most birthing units (80.3 %) had a system in place for reviewing SMM events. Although most review systems involved multidisciplinary expert panels with representation from birthing unit leads (82.0 %), nursing (78.0 %), and obstetrics (73.7 %), specialties, such as obstetric anaesthesia (42.4 %), midwifery (41.5 %), and internal medicine (16.9 %), were underrepresented. Lessons learned were rarely shared outside the hospital and never shared beyond regional health authorities. Importantly, 76.2 % of respondents were willing to contribute anonymized SMM data within a centralized reporting system. Conclusions: Most responding Canadian birthing units have a process in place to review SMM and would be willing to share anonymized data as part of a centralized initiative, thereby demonstrating the feasibility of leveraging existing infrastructures to establish CanOSS.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".