Prenatal cannabis exposure in the clinic and laboratory: What do we know and where do we need to go?
Bibliographic record
Abstract
Coincident with the legalisation of cannabis in many nations, rates of cannabis use during pregnancy have increased. Like prior investigations on smoking and alcohol, understanding how prenatal cannabis exposure (PCE) impacts offspring outcomes across the lifespan will be critical for informing choices for pregnant people, clinicians, and policy makers alike. A thorough characterization of the life-long impacts is especially urgent for supporting all of these stakeholders in the decision-making process. While studies in humans bring forth the most direct information, it can be difficult to parse the impact of PCE from confounding variables. Laboratory studies in animal models can provide experimental designs that allow for causal inferences to be drawn, however there can be challenges in designing experiments with external validity in mirroring real-world exposure, as well as challenges translating results from the laboratory back to the clinic. In this literature review, we first highlight what is known about patterns of cannabis use during pregnancy. We then seek to lay out updates to the current understanding of the impact of PCE on offspring development informed by both human and nonhuman animal experiments. Finally we highlight opportunities for information exchange among the laboratory, clinic, and policy, identifying gaps to be filled by future research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".