Psychedelics for the Treatment of Psychiatric Disorders: Interpreting and Translating Available Evidence and Guidance for Future Research
Bibliographic record
Abstract
During the past decade, there has been extraordinary public, media, and medical research interest in psychedelics as promising therapeutics for difficult-to-treat psychiatric disorders. Short-term controlled trial data suggest that certain psychedelics are effective and safe in the treatment of major depressive disorder, treatment-resistant depression, and posttraumatic stress disorder. Preliminary evidence also supports efficacy in other psychiatric disorders (e.g., tobacco and alcohol use disorders). Notwithstanding the interest and promise of psychedelics, concerns have arisen with respect to the interpretability and translatability of study results. For example, aspects related to short- and long-term safety, abuse liability, and the essentiality of the psychedelic "trip" and psychological support are, inter alia, insufficiently characterized with psychedelic agents. The overarching aims in this overview are 1) to review methodological aspects that affect inferences and interpretation of extant psychedelic studies in psychiatric disorders, and 2) to provide guidance for future research and development of psychedelic treatment in psychiatry, critical to study interpretation and clinical implementation.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".