Toxicological effects of cannabis on reproduction and human development
Bibliographic record
Abstract
Cannabis is a recreational drug that is also widely used for its potential therapeutic benefits. Cannabis use has increased in recent years, and products are increasingly accessible in jurisdictions where it has been legalized and commercially distributed. In addition, higher potency forms of cannabis are now frequently available compared to previous decades. In this paper, we discuss the epidemiology and toxicological aspects of cannabis use in relation to its impacts on reproductive function and offspring development. Cannabis and its chemical components THC and CBD demonstrate mild to moderate reproductive and developmental toxicity and adverse effects on exposed humans and animals. A robust biologic plausibility exists for a causal association between cannabis use and outcomes through ECS disruption and other mechanisms; however, the epidemiological data remain challenging to interpret due to potential confounding. Despite these limitations, continued monitoring of cannabis use in the human population for associations with reproductive and developmental outcomes remains an important public health concern, especially with recent increases in cannabis use prevalence and the move towards legalization in several countries. • Cannabis is increasing, and cannabis products are increasingly accessible. • The chemical components THC and CBD show reproductive and developmental toxicity in in vitro and in vivo models, with some epidemiologic evidence • Causal associations between cannabis and outcomes in humans may be confounded • Cannabis use is an evolving public health issue following legalization in several countries
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".