The impact of cannabinoids on reproductive function
Bibliographic record
Abstract
In brief: This review article summarizes the effects of pre- or peri-conceptual exposure to cannabinoids on female and male reproductive function, along with pregnancy outcomes from 2014 to 2024. In particular, it addresses the gaps in knowledge regarding the specific contributions of the major constituents of cannabis, THC and CBD, on reproduction. Abstract: With increased use of cannabis worldwide, especially in our young adult population, there is a great impetus to understand the impact of cannabis and its constituents (i.e. THC and CBD) on pregnancy, fetal outcomes and male and female reproductive function. This review assessed the current evidence (2014-2024) regarding the effects of cannabinoids on reproductive function (male, female and pregnancy) in animal and human studies. In particular, pre- or periconceptual exposure to cannabinoids were assessed to determine their effects across the lifespan along with transgenerational effects. From the outcomes of this review, we conclude there is a greater need for future preclinical and clinical studies to assess how various routes of cannabinoid exposure along with differing mixtures of cannabinoid constituents may interact to impede reproductive health. Collectively, the outcomes of these studies are important to clinicians and regulatory agencies in the context of functional evidence to support policy and decision-making regarding the safety of cannabis use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.005 | 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".