Mapping Psilocybin-Assisted Therapies: A Scoping Review
Bibliographic record
Abstract
Abstract We conducted a scoping review on psilocybin-assisted therapy for addiction, depression, anxiety and post-traumatic stress disorder. Psilocybin is a naturally-occurring tryptophan derivative found in species of mushroom with psycho-active properties. From 2022 records identified by database searching, 40 publications were included in the qualitative synthesis from which we identified 9 clinical trials with a total of 169 participants. Trials used a peak-psychedelic model of therapy, emphasizing inward journey through the use of eyeshades, set musical scores and with medium to high doses of psilocybin. No serious adverse effects were reported; mild adverse effects included transient anxiety, nausea and post-treatment headaches. Overall, the 9 trials all demonstrated safety, tolerability and preliminary efficacy in the treatments of obsessive-compulsive disorder, substance use disorder, treatment-resistant unipolar depression, anxiety or depression in patients with life-threatening cancer and demoralization among long-term AIDS survivors.The literature was found to be early and exploratory, with several limitations: only 5 were randomized controlled trials, small and homogenous patient sample size, difficulties in blinding, and the confounding influence of psychological supports provided. Further research is indicated to establish effectiveness for these and other indications, with a more diverse range of patients, and with differing program and dosing modalities.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".