Association between gestational cannabis exposure and maternal, perinatal, placental, and childhood outcomes
Bibliographic record
Abstract
Globally, the availability and formulations for the administration of cannabis are changing with decriminalization or legalization of recreational use in some jurisdictions, and the prescription of cannabis also occurring. These changes are likely to affect the prevalence of use, including by women of childbearing age. The effects of in utero and infant alcohol and tobacco exposure are well-documented, but the outcomes of cannabis exposure are less certain. The content of delta-9-tetrahydrocannabinol (THC), the psychoactive component of cannabis has progressively increased over several decades. This review explores the limited knowledge surrounding the epidemiology of gestational and postnatal cannabis exposure and implications for the mother-placenta-fetus/neonate triad. We examine cannabis' effects from antenatal and lactation exposure on (a) pregnancy and perinatal outcomes, (b) placental health, and (c) longer term cardiometabolic and neurodevelopmental risks and outcomes. Though definitive outcomes are lacking, gestational cannabis has been associated with increased risk of other substance use during pregnancy; impaired placental blood flow; increased risk of small for gestational age births; and associated complications. Childhood and adolescent outcomes are sparsely assessed, with suggested outcomes including increased risk of depression and attention-deficit hyperactivity disorder. Cardiometabolic implications of gestational cannabis use may include maternal fatty liver, obesity, insulin resistance, and increased risk of gestational diabetes mellitus (GDM), with potential consequences for the fetus. Clinical implications for pediatric practice were explored in a bid to understand any potential risk or impact on child health and development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".