Identifying the effect of inherited bleeding disorders on the development of postpartum hemorrhage: a population-based, retrospective cohort study
Bibliographic record
Abstract
Background: Women with inherited bleeding disorders (IBDs) are at an increased risk of postpartum hemorrhage (PPH). However, the impact of other maternal predelivery risk factors, including anemia, on the association between IBD and maternal bleeding remains poorly understood. Additionally, studies examining potential pathways linking IBD and PPH are limited. Objectives: We aimed to determine the risk of PPH associated with IBD. Methods: A retrospective cohort study was conducted using data held within ICES (formerly the Institute for Clinical Evaluative Sciences). Women with an in-hospital, live, or stillborn delivery between January 2014 and December 2019 were included. Poisson regression with robust error variance was used to determine the risk (RR) and 95% CIs of PPH among women with or without an IBD diagnosis. Models were stratified for primiparous and multiparous women. Results: Among the total population of 601,773 women, 29,661 (4.93%) experienced PPH. Multivariate models demonstrated that IBD was an independent risk factor for PPH among both the total cohort (adjusted RR [aRR] = 1.26; 95% CI: 1.08, 1.46) and primiparous women (aRR = 1.36; 95% CI: 1.12, 1.66). Among multiparous women, prior PPH was associated with an increased risk of PPH (aRR = 8.65; 95% CI: 8.32, 8.99), whereas IBD had no effect (aRR = 1.1; 95% CI: 0.86, 1.4). Predelivery anemia, placental conditions, multifetal gestation, and induction of labor were associated with increased PPH risk among all cohorts. Conclusions: IBD significantly increases the risk of PPH. The management of delivery should be based on individualized assessment of risk factors to ensure optimal maternal outcomes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".