Impact of inherited bleeding disorders on maternal bleeding and other pregnancy outcomes: A population‐based cohort study
Bibliographic record
Abstract
INTRODUCTION: Increasing rate of postpartum haemorrhage (PPH) has been observed between 2003 and 2010 in Canada. Inherited bleeding disorders contribute to the risk of PPH. AIM: To identify the trend in PPH in the last decade, assess the impact of bleeding disorders on pregnancy outcomes and evaluate their coagulation workup during pregnancy. METHODS: We conducted a population-based retrospective cohort study using the Alberta Pregnancy Birth Cohort from 2010 to 2018. We included women with von Willebrand disease (VWD) and haemophilia, identified by previously validated algorithm and matched with controls. Logistic regression was used to compute odds of PPH and other pregnancy outcomes. RESULTS: We identified 311,330 women with a total of 454,400 pregnancies with live births. The rate of PPH did not change significantly from 10.13 per 100 deliveries (95% CI 10.10-10.16) in 2010-10.72 (95% CI 10.69-10.75) in 2018 (p for trend = .35). Women with bleeding disorders were significantly more likely to experience PPH (odds ratio [OR] 2.3; 95% CI 1.5-3.6), antepartum haemorrhage (OR 2.9; 95% CI 1.5-5.9) and red cell transfusion (OR 2.8; 95% CI 1.1-7.0). We observed a nonsignificant rise in the rate of PPH in women with VWD and haemophilia. Only 49.5% pregnancies with bleeding disorders had third trimester coagulation factor levels checked. Higher odds of PPH and antepartum haemorrhage were observed even with factor levels ≥0.50 IU/mL in third trimester. CONCLUSION: Despite comprehensive care in women with bleeding disorders, they are still at higher risk of adverse pregnancy outcomes compared to population controls.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".