Prevalence and outcomes of prenatal recreational cannabis use in high‐income countries: a scoping review
Bibliographic record
Abstract
BACKGROUND: With expanding recreational cannabis legalisation, pregnant women and their offspring are at risk of potentially harmful consequences. OBJECTIVES: To assess the prevalence of recreational cannabis use among pregnant women, health outcomes associated with prenatal recreational cannabis use, and the potential impact of recreational cannabis legalisation on this population. SEARCH STRATEGY: Five databases and the grey literature were systematically searched (2000-2019). SELECTION CRITERIA: Human studies published in English or French reporting on the prevalence of prenatal recreational cannabis use in high-income countries. DATA COLLECTION AND ANALYSIS: Data on study characteristics, prenatal substance use, and health outcomes were extracted and qualitatively synthesised. MAIN RESULTS: Forty-one publications met our inclusion criteria. The overall prevalence of prenatal cannabis use varied substantially (min-max: 0.24-22.6%), with the greatest use in the first trimester. In the three studies with temporal data available, rates of prenatal cannabis use increased across years. Only 7/41 and 5/41 studies provided information on gestational age of exposure and frequency of use, respectively. The concomitant use of alcohol, illicit drugs, and tobacco was higher among cannabis users than nonusers. Prenatal cannabis use was associated with select neonatal, but not maternal, health outcomes. There were insufficient data to compare prenatal cannabis use between the pre- and post-legalisation periods. CONCLUSION: Cannabis use among pregnant women is prevalent and may be associated with adverse neonatal outcomes. Future studies should assess the gestational age and frequency of cannabis exposure, and usage patterns prior to and following legalisation. TWEETABLE ABSTRACT: Women who consume cannabis during pregnancy could risk predisposing their newborns to poor birth 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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".