Can Drug Effects Explain the Recent Temporal Increase in Atonic Postpartum Haemorrhage?
Bibliographic record
Abstract
BACKGROUND: Rates of postpartum haemorrhage and atonic postpartum haemorrhage have increased in several high-income countries. We carried out a study to examine if drug use in pregnancy, or drug and other interactions, explained this increase in postpartum haemorrhage. METHODS: The linked administrative and hospital databases of the Québec Pregnancy Cohort were used to define a cohort of pregnant women in Québec, Canada, from 1998 to 2009 (n = 138,704). Case-control studies on any postpartum haemorrhage and atonic postpartum haemorrhage were carried out within this population, with up to five controls randomly selected for each case after matching on index date and hospital of delivery (incidence density sampling). Conditional logistic regression was used to estimate the effects of drug use on postpartum haemorrhage and atonic postpartum haemorrhage. RESULTS: There was an unexpected non-linear, declining temporal pattern in postpartum haemorrhage and atonic postpartum haemorrhage between 1998 and 2009. Use of antidepressants (mainly selective serotonin reuptake inhibitors) was associated with higher rates of postpartum haemorrhage [adjusted rate ratio (aRR) 1.48, 95% confidence interval (CI) 1.23, 1.77] and atonic postpartum haemorrhage [aRR 1.40, 95% CI 1.13, 1.74]. Thrombocytopenia was also associated with higher rates of postpartum haemorrhage [aRR 1.52, 95% CI 1.16, 2.00]. There were no statistically significant drug interactions. Adjustment for maternal factors and drug use had little effect on temporal trends in postpartum haemorrhage and atonic postpartum haemorrhage. CONCLUSIONS: Although antidepressant use and thrombocytopenia were associated with higher rates of atonic postpartum haemorrhage, antidepressant and other drug use did not explain temporal trends in postpartum haemorrhage.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".