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Record W2141081839 · doi:10.1136/bmj.h2298

Antipsychotic drug use in pregnancy: high dimensional, propensity matched, population based cohort study

2015· article· en· W2141081839 on OpenAlexafffundabout
Simone N. Vigod, Tara Gomes, Andrew S. Wilton, Valerie H. Taylor, Joel G. Ray

Bibliographic record

VenueBMJ · 2015
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsSt. Michael's HospitalInstitute for Clinical Evaluative SciencesWomen's College HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative Sciences
KeywordsMedicinePregnancyAntipsychoticPoisson regressionObstetricsGestational diabetesPopulationPropensity score matchingRate ratioCohort studyMedical prescriptionCohortConfidence intervalPediatricsInternal medicineSchizophrenia (object-oriented programming)PsychiatryGestationPharmacology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate maternal medical and perinatal outcomes associated with antipsychotic drug use in pregnancy. DESIGN: High dimensional propensity score (HDPS) matched cohort study. SETTING: Multiple linked population health administrative databases in the entire province of Ontario, Canada. PARTICIPANTS: Among women who delivered a singleton infant between 2003 and 2012, and who were eligible for provincially funded drug coverage, those with ≥ 2 consecutive prescriptions for an antipsychotic medication during pregnancy, at least one of which was filled in the first or second trimester, were selected. Of these antipsychotic drug users, 1021 were matched 1:1 with 1021 non-users by means of a HDPS algorithm. MAIN OUTCOME MEASURES: The main maternal medical outcomes were gestational diabetes, hypertensive disorders of pregnancy, and venous thromboembolism. The main perinatal outcomes were preterm birth (<37 weeks), and a birth weight <3rd or >97th centile. Conditional Poisson regression analysis was used to generate rate ratios and 95% confidence intervals, adjusting for additionally prescribed non-antipsychotic psychotropic medications. RESULTS: Compared with non-users, women prescribed an antipsychotic medication in pregnancy did not seem to be at higher risk of gestational diabetes (rate ratio 1.10 (95% CI 0.77 to 1.57)), hypertensive disorders of pregnancy (1.12 (0.70 to 1.78)), or venous thromboembolism (0.95 (0.40 to 2.27)). The preterm birth rate, though high among antipsychotic users (14.5%) and matched non-users (14.3%), was not relatively different (rate ratio 0.99 (0.78 to 1.26)). Neither birth weight <3rd centile or >97th centile was associated with antipsychotic drug use in pregnancy (rate ratios 1.21 (0.81 to 1.82) and 1.26 (0.69 to 2.29) respectively). CONCLUSIONS: Antipsychotic drug use in pregnancy had minimal evident impact on important maternal medical and short term perinatal outcomes. However, the rate of adverse outcomes is high enough to warrant careful assessment of maternal and fetal wellbeing among women prescribed an antipsychotic drug in pregnancy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.069
GPT teacher head0.340
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations159
Published2015
Admission routes3
Has abstractyes

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