Adverse event reporting and management in psilocybin therapy clinical trials: A systematic review to guide clinical and research protocol development
Bibliographic record
Abstract
Psilocybin, a psychedelic prodrug, has gained renewed interest for its potential to treat various psychiatric disorders, including depression, anxiety, and substance use disorders. While promising, concerns remain regarding its safety profile and the management of potential adverse events (AEs). This systematic review aimed to evaluate the incidence, nature, and severity of adverse events and serious adverse events (SAEs) associated with psilocybin use across diverse clinical populations. A comprehensive search was conducted across MEDLINE, Embase, and APA PsycInfo via the OVID platform, from database inception to June 5, 2024. A total of 42 clinical studies (N = 1068 participants) met inclusion criteria, all of which reported on AEs and/or SAEs following psilocybin administration. All studies were deemed to have a high risk of bias due to concerns regarding blinding. We synthesized information on common, uncommon, and SAEs, instances of suicidal ideation, methods of measuring AEs, and AEs requiring medical intervention. Reported AEs included headache, transient increases in blood pressure, and nausea, which typically resolved on their own. In rare instances, medical intervention was required. SAEs were reported infrequently in 2 of 42 studies and were limited to participants with underlying depressive disorders (e.g., suicidal behaviour, hospitalization). Overall, psilocybin appears to have a favourable safety profile when administered in controlled settings. Based on our findings, we provide an outline of commonly reported AEs, uncommon AEs, SAEs, and considerations for future clinical and research protocols.
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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 | 0.116 | 0.187 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.020 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".