Psilocybin-assisted therapy for depression: A systematic review and dose-response meta-analysis of human studies
Bibliographic record
Abstract
Psilocybin is increasingly studied for its antidepressant effect, but its optimal dosage for depression remains unclear. We conducted a systematic review and a dose-response meta-analysis to find the optimal dosage of psilocybin to reduce depression scores. Following our protocol (CRD 42022220190) multiple electronic databases were searched from their inception until February 2023, to identify double-blind randomized placebo-controlled (RCTs) fixed-dose trials evaluating the use of psilocybin for adult patients with primary or secondary depression. A one-stage dose-response meta-analysis with restricted cubic splines was used. Cochrane risk of bias was used to assess risk of bias. Our analysis included seven studies with a total of 489 participants. Among these, four studies focused on primary depression (N = 366), including one study with patients suffering from treatment-resistant depression. The remaining three studies examined secondary depression (N = 123). The determined 95% effective doses per day (ED95) were 8.92, 24.68, and 36.08 mg/70 kg for patients with secondary depression, primary depression, and both subgroups, respectively. We observed significant dose-response associations for all curves, each plateauing at different levels, except for the bell-shaped curve observed in the case of secondary depression. Additionally, we found significant dose-response associations for various side effects, including physical discomfort, blood pressure increase, nausea/vomiting, headache/migraine, and the risk of prolonged psychosis. In conclusion, we discovered specific ED95 values for different populations, indicating higher ED95 values for treatment-resistant depression, primary depression, and secondary depression groups. Further RCTs are necessary for each population to determine the optimal dosage, allowing for maximum efficacy while minimizing side effects.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".