Adverse events associated with classic psychedelics and MDMA: a real-world population-based study using the WHO pharmacovigilance database (VigiBase)
Bibliographic record
Abstract
Psychedelic use has greatly increased within clinical and recreational settings over recent years. While demonstrating a favorable safety profile within certain clinical populations, little empirical research has explored safety of psychedelic use within real-world samples. Using the World Health Organization (WHO) VigiBase, a comprehensive global pharmacovigilance database with voluntary spontaneous reporting of adverse events (AEs) from real-world clinical and recreational populations, we examined reports for classic psychedelics and MDMA. Most reports were made for MDMA (n = 1573) and LSD (n = 394), while psilocybin (n = 56), DMT (n = 18), and mescaline (n = 15) had fewer reports. The most common AEs for all substances were psychiatric in nature, specifically surrounding substance or drug abuse and dependence. Reports of overdose constituted 1.1 to 1.7 % of total AEs. Pregnancy-related and congenital disorders were rare. Compared to the acetaminophen control, LSD and MDMA were associated with significantly greater odds for the reported AEs of alcohol abuse (LSD: ROR=45.7, 95 % CI: 27.2 - 76.9; MDMA: ROR=19.2, 95 % CI: 12.2 - 30.4), substance use disorder (LSD: ROR=71.1, 95 % CI: 36.3 - 139.2; MDMA: ROR=129.9, 95 % CI: 78.4 - 215.5) and substance dependence (LSD: ROR=215.1, 95 % CI: 69.0 - 670.3; MDMA: ROR=76.8, 95 % CI: 25.5 - 231.8). These reports were also greater than those associated with the external positive control, oxycodone. Taken together, this exploratory study provides the first analysis of AEs associated with psychedelics reported to a global pharmacovigilance database and can inform their real-world safety. Findings should be considered in light of limitations surrounding co-use of other substances and potential deterrence towards reporting use of illicit substances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".