Use of antidepressants in pregnancy
Bibliographic record
Abstract
does not increase risk of postpartum haemorrhage QUESTION Question: Do selective serotonin reuptake inhibitors (SSRIs) and other antidepressants increase risk of postpartum haemorrhage if taken in late pregnancy? People: 28 863 Canadian women aged 16–45 years who received government-funded prescription cover for drugs in the 2 years prior to the delivery of their baby identified through the Ontario Drug Benefit (ODB) database. Cases (n = 2460) were women in this cohort with a diagnosis of postpartum haemorrhage following a vaginal or caesarean delivery (as reflected in the Canadian Institute for Health Information Discharge Abstract Database); if haemorrhage occurred in more than one pregnancy, only the first was included. Exclusions: several medical conditions including alcoholism, liver disease, thrombosis, malignant neoplasia, pulmonary embolism, platelet defects, and hereditary bleeding disorders and exposure to particular drugs (antipsychotics, anticonvulsants, anticoagulants, monoamine oxidase inhibitors, more than one antidepressant, mood stabilisers, antiplatelets and systemic corticosteroids). Control women (26 403; up to 10 for each case) were women with uncomplicated deliveries matched to cases for age, date, delivery method and number of deliveries in the preceding 7 years. Setting: Ontario, Canada; databases used to collect information on episodes occurring between January 1999
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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 | 0.000 | 0.006 |
| 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 | 0.000 |
| 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".