Maternal cannabis use during pregnancy and maternal and neonatal outcomes: A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between reported prenatal cannabis use and neonatal and maternal outcomes and whether the legalisation of cannabis in Canada affected the rates of reported use or the association with maternal and neonatal outcomes. DESIGN: Population-based retrospective cohort study. SETTING: Routinely collected data in a real-world setting. POPULATION: All women in the Canadian province of Nova Scotia with singleton births between 1 January 2004 and 30 June 2021. METHODS: The association between cannabis use and maternal and neonatal outcomes was examined using generalised linear models with inverse probability weighting. MAIN OUTCOME MEASURES: Maternal and neonatal outcomes in the peripartum and postpartum period. RESULTS: Rates of reported cannabis use in pregnancy increased from 1.3% to 7.5% over the study period with no appreciable change in slope after legalisation in 2018. Infants of mothers reporting cannabis use in pregnancy were more likely to have major anomalies and a 5-minute Apgar score ≤7, require neonatal intensive care unit admission, and had lower birthweight, head circumference and birth length than infants of mothers not reporting cannabis use. These associations did not differ before and after legalisation. CONCLUSIONS: Reported cannabis use during pregnancy is associated with early postnatal complications and reduced fetal growth, even after taking into account a range of confounding factors. Rates of reported cannabis use during pregnancy increased over the past 5 years in Nova Scotia with no apparent additional effect of legalisation.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".