Prenatal Cannabis Use and Neonatal Outcomes
Bibliographic record
Abstract
Importance: Prenatal cannabis use continues to increase, and cannabis remains the most commonly used illegal substance in pregnancy. Accumulating evidence suggests potential adverse effects on fetal and neonatal outcomes following cannabis use in pregnancy. Objective: To update a living systematic review and meta-analysis to provide a timely understanding regarding cannabis use in pregnancy and fetal and neonatal outcomes. Data Sources: The previous review was updated by searching bibliographic databases MEDLINE, CINAHL, PsycInfo, Global Health, and Evidence-Based Medicine Reviews Cochrane Database of Systematic Reviews from November 1, 2021, through April 4, 2024. Study Selection: Cohort or case-control studies comparing pregnancies with and without prenatal cannabis use on prespecified fetal or neonatal outcomes with adjustment for confounders, such as co-use of tobacco products, were included. Two independent reviewers screened studies, with disagreements resolved through discussion. Data Extraction and Synthesis: Included studies were extracted by 1 reviewer and confirmed by a second. Risk of bias was assessed with the Newcastle-Ottawa Scale. Random-effects meta-analyses of unadjusted and adjusted odds ratios (ORs) were performed for all primary outcomes. Results were synthesized using the Grading of Recommendations Assessment, Development, and Evaluation approach. Main Outcomes and Measures: Primary outcomes were preterm birth (PTB; <37 weeks of gestation), small for gestational age (SGA), low birth weight (LBW; <2500 g), and perinatal mortality. Results: For this update, 8 new studies with 1 709 998 participants were added, for a total of 51 studies synthesized (N = 21 146 938). From meta-analyses of adjusted effect sizes, moderate-certainty evidence indicated that cannabis use in pregnancy was associated with increased odds of LBW (20 studies; OR, 1.75; 95% CI, 1.41-2.18), PTB (20 studies; OR, 1.52; 95% CI, 1.26-1.83), and SGA (12 studies; OR, 1.57; 95% CI, 1.36-1.81), and low-certainty evidence indicated that it was associated with greater odds of perinatal mortality (6 studies; OR, 1.29; 95% CI, 1.07-1.55). Previously, the evidence was rated as very low or low certainty. Conclusions and Relevance: Cannabis use in pregnancy was associated with greater odds of PTB, SGA, and LBW even after adjusting for co-use of tobacco products, and confidence in these findings increased from low in the prior review to moderate in the current meta-analysis. The findings of this study may help inform patient counseling and future public health policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".