Evaluating the Risk of Psilocybin for the Treatment of Bipolar Depression: A Review of the Research Literature and Published Case Studies
Bibliographic record
Abstract
Abstract Growing evidence suggests that psilocybin, the active ingredient in hallucinogenic mushrooms, can rapidly and durably improve symptoms of depression, leading to recent breakthrough status designation by the FDA and legalization for mental health treatment in some jurisdictions. Depression in bipolar disorder is associated with significant morbidity and has few effective treatments. However, there is little available scientific data on the risk of psilocybin use in people with bipolar disorder. Individuals with bipolar disorder have been excluded from modern clinical trials, out of understandable concerns of activating mania or worsening the illness course. As psilocybin becomes more available, people with these disorders will likely seek psilocybin treatment for depression and have likely already been doing so in unregulated settings. Our goal here is to summarize the known risks of psilocybin use (and similar substances) in bipolar disorder and to systematically evaluate examples of published case history data, in order to critically evaluate the relative risk of psilocybin as a treatment for bipolar depression. We found 17 cases suggesting that there is potential risk for activating a manic episode, thereby warranting caution. Nonetheless, the relative lack of systematic data or common case examples indicating risk appears to show that a cautious trial, using modern trial methods focusing on appropriate ‘set’ and ‘setting’, targeted at those lowest at risk for mania in the bipolar spectrum (e.g., bipolar 2 disorder), is very much needed, especially given the degree to which depression impacts this population.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".