The effect of prenatal cannabis exposure on offspring preterm birth: a cumulative meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Mixed results have been reported on the association between prenatal cannabis exposure and preterm birth. This study aimed to examine the magnitude and consistency of associations reported between prenatal cannabis exposure and preterm birth. METHODS: This review was guided by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We performed a comprehensive search of the literature on the following electronic databases: PubMed, EMBASE, SCOPUS, Psych-INFO and Web of Science. The revised version of the Newcastle-Ottawa Scale (NOS) was used to appraise the methodological quality of the studies included in this review. Inverse variance weighted random-effects cumulative meta-analysis was undertaken to pool adjusted odds ratios (aOR) after sequential inclusion of each newly published study over time. The OR and 95% confidence interval (CI) limits required (stability threshold) for a new study to move the cumulative odds ratio to the null were also computed. RESULTS: A total of 27 observational studies published between 1986 and 2022 were included in the final cumulative meta-analysis. The sample size of the studies ranged from 304 to 4.83 million births. Prenatal cannabis exposure was associated with an increased risk of preterm birth (pooled aOR = 1.35, 95% CI = 1.24-1.48). The stability threshold was 0.74 (95% CI limit = 0.81) by the end of 2022. CONCLUSIONS: Offspring exposed to maternal prenatal cannabis use was associated with higher risk of preterm birth, which warrants public health messages to avoid such exposure, particularly during pregnancy.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.056 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".