Psilocybin for clinical indications: A scoping review
Bibliographic record
Abstract
BACKGROUND: Psychedelic drugs have been of interest in medicine since the early 1950s. There has recently been a resurgence of interest in psychedelics. AIMS: The objective of this study is to determine the extent of the available literature on psilocybin for medical indications including the designs used, study characteristics, indications studied, doses, and authors' conclusions. We identify areas for further study where there are research gaps. METHODS: We conducted a systematic scoping review of clinical indications for psilocybin, encompassing psychiatric and medical conditions. We systematically searched Medline and Embase using keywords related to psilocybin. We reviewed titles and texts in duplicate using Covidence software. We extracted data individually in duplicate using Covidence software and a senior reviewer resolved all author conflicts. We analyzed data descriptively. RESULTS: We included 193 published and 80 ongoing studies. Thirty-seven percent of included studies were systematic reviews. Only 12% of included studies were randomized controlled trials. The median number of participants was 22 with a median of 18 participants who had taken psilocybin. Thirty-eight percent of studies reported at least one potential conflict of interest. The most common indication was depression (28%). Also commonly studied were substance use (14%), mental health in life-threatening illness (9%), headaches (6%), depression and anxiety (6%), obsessive-compulsive disorder (3%), and anxiety disorders (3%). CONCLUSIONS: Most studies involving the administration of psilocybin have small sample sizes and the most common focus has been psychiatric disorders. There is a need for high-quality randomized trials on psilocybin and to expand consideration to other promising indications, such as chronic pain.
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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".