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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".