Cannabis impacts female fertility as evidenced by an in vitro investigation and a case-control study
Bibliographic record
Abstract
Cannabis consumption and legalization is increasing globally, raising concerns about its impact on fertility. In humans, we previously demonstrated that tetrahydrocannabinol (THC) and its metabolites reach the ovarian follicle. An extensive body of literature describes THC’s impact on sperm, however no such studies have determined its effects on the oocyte. Herein, we investigate the impact of THC on human female fertility through both a clinical and in vitro analysis. In a case-control study, we show that follicular fluid THC concentration is positively correlated with oocyte maturation and THC-positive patients exhibit significantly lower embryo euploid rates than their matched controls. In vitro, we observe a similar, but non-significant, increased oocyte maturation rate following THC exposure and altered expression of key genes implicated in extracellular matrix remodeling, inflammation, and chromosome segregation. Furthermore, THC induces oocyte chromosome segregation errors and increases abnormal spindle morphology. Finally, this study highlights potential risks associated with cannabis use for female fertility. Cannabis use has been reported to impair sperm quality but less is known about whether cannabis affects female fertility. Here the authors report that cannabis use and THC levels associate with oocyte maturation rate and reduced number of euploid embryos in a retrospective case-control study of patients undergoing IVF treatment, while in vitro data suggests THC impairs chromosome segregation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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, 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".