Determining the incidence of postpartum haemorrhage among Ontario women with and without inherited bleeding disorders: A population‐based cohort study
Bibliographic record
Abstract
INTRODUCTION: At a population level, there is a poor understanding of the incidence and pre-disposing risk factors of postpartum haemorrhage (PPH) among women with inherited bleeding disorders (IBD). AIM: To determine the incidence of PPH, and identify maternal factors associated with risk of PPH among women with IBD. METHODS: We conducted a retrospective cohort study using data housed within ICES (formerly known as the Institute for Clinical Evaluative Sciences). The cohort included women with an in-hospital, live or stillborn delivery, between January 2014 and December 2019. The primary outcome was PPH (identified by ICD-10 code O72). PPH incidence and risk factors were compared between women with and without IBD. Temporal trends were assessed using the Cochrane-Armitage test. Between group differences were assessed using standardised differences (std. difference). RESULTS: Total 601,773 women were included; 2002 (.33%) had an IBD diagnosis. PPH incidence was 1.5 times higher (7.3 vs. 4.9 cases/100 deliveries, std. difference .1) among women with IBD compared to women without. Women with IBD were slightly older (31.7 vs. 30.7 years), had higher rates of hypertension, previous PPH, and induction of labour. Women with IBD were more frequently diagnosed with anaemia (4.8% vs. 1.8%; std difference .17) and had lower haemoglobin levels at admission for delivery compared to women without IBD. CONCLUSIONS: This study contributes to the literature regarding obstetric bleeding among women with IBD, showing that anaemia at delivery may be an important risk factor for PPH. Given their predisposition to anaemia, clarifying this relationship will optimise management and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".