Prenatal Cannabinoid Exposure: Emerging Evidence of Physiological and Neuropsychiatric Abnormalities
Bibliographic record
Abstract
Clinical reports of cannabis use prevalence during pregnancy vary widely from 3% to upwards of 35% in North America; this disparity likely owing to underestimates from self-reporting in many cases. The rise in cannabis use is mirrored by increasing global legalization and the overall perceptions of safety, even during pregnancy. These trends are further compounded by a lack of evidence-based policy and guidelines for prenatal cannabis use, which has led to inconsistent messaging by healthcare providers and medically licensed cannabis dispensaries regarding prenatal cannabis use for treatment of symptoms, such as nausea. Additionally, the use of cannabis to self-medicate depression and anxiety during pregnancy is a growing medical concern. This review aims to summarize recent findings of clinical and preclinical data on neonatal outcomes, as well as long-term physiological and neurodevelopmental outcomes of prenatal cannabis exposure. Although many of the outcomes under investigation have produced mixed results, we consider these data in light of the unique challenges facing cannabis research. In particular, the limited longitudinal clinical studies available have not previously accounted for the exponential increase in (-)-Δ9- tetrahydrocannabinol (Δ9-THC; the psychoactive compound in cannabis) concentrations found in cannabis over the past two decades. Polydrug use and the long-term effects of individual cannabis constituents [Δ9-THC vs. cannabidiol (CBD)] are also understudied, along with sex-dependent outcomes. Despite these limitations, prenatal cannabis exposure has been linked to low birth weight, and emerging evidence suggests that prenatal exposure to Δ9-THC, which crosses the placenta and impacts placental development, may have wide-ranging physiological and neurodevelopmental consequences. The long-term effects of these changes require more rigorous investigation, though early reports suggest Δ9-THC increases the risk of cognitive impairment and neuropsychiatric disease, including psychosis, depression, anxiety, and sleep disorders. In light of the current trends in the perception and use of cannabis during pregnancy, we emphasize the social and medical imperative for more rigorous investigation of the long-term effects of prenatal cannabis exposure.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".