Therapeutic uses of psychedelics for eating disorders and body dysmorphic disorder
Bibliographic record
Abstract
BACKGROUND: Clinical use of psychedelics has gained considerable attention, with promising benefits across a range of mental disorders. Current pharmacological and psychotherapeutic treatments for body dysmorphic disorder (BDD) and eating disorders (EDs) have limited efficacy. As such, other treatment options such as psychedelic-assisted therapies are being explored in these clinical groups. AIMS: This systematic review evaluates evidence related to the therapeutic potential of psychedelics in individuals diagnosed with BDD and EDs. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review of all study designs published to the end of February 2022 that identified changes in ED/BDD symptom severity from psychedelics using validated measures to assess symptom changes. RESULTS: Our search detected a total of 372 studies, of which five met inclusion criteria (two exploratory studies, two case reports, and one prospective study). These were included in the data evaluation. Effects of psychedelics on BDD and various ED symptoms were identified mostly through thematic analyses and self-reports. CONCLUSIONS: Our findings highlight that more research is needed to determine the safety and efficacy of psychedelics in BDD and EDs and we suggest avenues for future exploration.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".