Effects of prenatal cannabis exposure on offspring mental health: A focus on the role of the immune system
Bibliographic record
Abstract
Globally, there is increasing legalization, permissiveness and acceptability of medical and recreational cannabis use-including among pregnant people. Yet, we lack a full understanding of cannabis' effects, including during pregnancy and in offspring who experienced prenatal cannabis exposure (PCE), despite decades of research. In particular, the ability of PCE to impact offspring mental health has been investigated in literature. Given the emerging link between mental health and immune system functioning (both centrally and peripherally) and that cannabinoid receptors are abundantly expressed in the immune system, it may be the case that PCE alters offspring mental health by impacting the immune system. In this review, we highlight current research on the effects of PCE and discuss differences among administration methods and across species, with a particular focus on changes in the immune system-focusing both on immune processes in the central nervous system and peripherally-relevant to altered offspring mental health. While the field is too nascent for robust conclusions, some interesting findings have been reported. Particularly in the placenta, in both humans and rodents (using vaporized exposure paradigms), cannabis use during pregnancy is associated with a reduction in pro-inflammatory cytokine levels, which is linked, in humans, to increased offspring anxiety, aggression and hyperactivity behaviors. Whereas data with regards to central nervous system immune cells, i.e., microglia, is limited, PCE seems to impact T cell dynamics in various organs. However, the link between offspring immune cell changes and offspring mental health requires further establishment. We conclude by discussing important future directions and a focus on harm reduction.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
| grok | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
| opus | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".