Psilocybin-assisted psychotherapy as a potential treatment for eating disorders: a narrative review of preliminary evidence
Bibliographic record
Abstract
Eating disorders (ED) are a group of potentially severe mental disorders characterized by abnormal energy balance, cognitive dysfunction, and emotional distress. Cognitive inflexibility is a major challenge to successful ED treatment and dysregulated serotonergic function has been implicated in this symptomatic dimension. Moreover, there are few effective treatment options and long-term remission of ED symptoms is difficult to achieve. There is emerging evidence for the use of psychedelic-assisted psychotherapy (PAP) for a range of mental disorders. Psilocybin is a serotonergic psychedelic that has demonstrated therapeutic benefit in a variety of psychiatric illnesses characterized by rigid thought patterns and treatment resistance. The current paper presents a narrative review of the hypothesis that psilocybin may be an effective adjunctive treatment for individuals with EDs, based on biological plausibility, transdiagnostic evidence, and preliminary results. Limitations of the PAP model and proposed future directions for its application to eating behavior are also discussed. Although the literature to date is not sufficient to propose the incorporation of psilocybin in the treatment of disordered eating behaviors, preliminary evidence supports the need for more rigorous clinical trials as an important avenue for future investigation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".