Postpartum Haemorrhage in Canada and France: A Population-Based Comparison
Bibliographic record
Abstract
OBJECTIVE: Maternal mortality ratio due to postpartum haemorrhage (PPH) is higher in France than in Canada. We explored this difference by comparing PPH features between these two countries. METHODS: Using data between 2004 and 2006, we compared the incidence, risk factors, causes and use of second-line treatments, of PPH between France (N = 6,660 PPH) and Canada (N = 9,838 PPH). We assessed factors associated with PPH through multivariate logistic models. RESULTS: PPH incidence, overall (4.8% (95% CI 4.7-4.9) in Canada and 4.5% (95% CI 4.4-4.7) in France), and after vaginal delivery (5.3% (95%CI 5.2-5.4) in Canada and 4.8 (95%CI 4.7-4.9) in France), were significantly higher in Canada than in France, but not after caesarean delivery. Women delivering without PPH were similar between the two populations, except for macrosomia (11% in Canada, 7% in France, p<0.001), caesarean delivery (27% in Canada, 18% in France, p<0.001), and episiotomy (17% in Canada, 34% in France, p<0.001). After vaginal delivery, factors strongly associated with PPH were multiple pregnancy, operative delivery and macrosomia in both populations, and episiotomy only in France (Odds Ratio 1.39 (95% CI 1.23-1.57)). The use of second-line treatments for PPH management was significantly more frequent in France than in Canada after both vaginal and caesarean delivery. CONCLUSION: PPH incidence was not higher in France than in Canada and there was no substantial difference in PPH risk factors between the 2 countries. Greater use of second-line treatments in PPH management in France suggests a more frequent failure of first-line treatments and a higher rate of severe PPH, which may be involved in the higher maternal mortality ratio due to PPH.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".