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Efficacy, all-cause discontinuation, and safety of serotonergic psychedelics and MDMA to treat mental disorders: A living systematic review with meta-analysis

2025· review· en· W4416015783 on OpenAlexafffund
Mikkel Højlund, Helin Yılmaz Kafalı, Begüm Kırmızı, Paolo Fusar‐Poli, Christoph U. Correll, Samuele Cortese, Michel Sabé, Jess G. Fiedorowicz, Gayatri Saraf, Josephine Zein, Michael Berk, Muhammad Ishrat Husain, Joshua D. Rosenblat, Ruby Rubaiyat, Kim Corace, Stanley Wong, Simon Hatcher, Mark Kaluzienski, Lakshmi N. Yatham, Andrea Cipriani, Corentin J. Gosling, Robin Carhart‐Harris, Peter Tanuseputro, Daniel T. Myran, Nicholas Fabiano, David Moher, Leah M. Mayo, Stuart G. Nicholls, Tracy White, Michele De Prisco, Joaquim Raduà, Eduard Vieta, Karim S. Ladha, Jay Katz, Areti Angeliki Veroniki, Marco Solmi

Bibliographic record

VenueEuropean Neuropsychopharmacology · 2025
Typereview
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsUniversity of CalgaryUniversity of British ColumbiaSt. Michael's HospitalCanadian Centre on Substance Use and AddictionCentre for Addiction and Mental HealthUniversity of OttawaUniversity of TorontoUniversity Health NetworkOttawa Hospital
FundersResearch Executive AgencyUniversity of TorontoDepartment of Health and Social CareCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchNational Health and Medical Research CouncilDepartment of Psychiatry, University of TorontoOttawa Hospital Research InstituteFaculty of Medicine, University of OttawaUniversity of Ottawa
KeywordsMDMASerotonergicPsilocybinHallucinogenDiscontinuationAnxietyRandomized controlled trialAbstinence

Abstract

fetched live from OpenAlex

Serotonergic psychedelics and 3,4-methylendioxtmethamphetamine (MDMA) are promising treatments for mental disorders with a continuously evolving evidence base. We searched Pubmed/Scopus/clinical trial registries up to 08july2025 for double-blind randomized controlled trials (RCTs) testing MDMA or serotonergic psychedelics in patients with mental disorders. Primary outcomes were change in disease-specific symptoms and all-cause discontinuation. Standardized mean differences (SMD) and relative risk (RR) were estimated using random-effects meta-analysis. Risk of bias (RoB) was assessed with Cochrane’s RoB-tool version 2 and certainty of evidence with GRADE. The review is maintained as living systematic review ( https://ebipsyche-database.org/ ). We included 30 RCTs (1480 participants; female=45.8 %; with psychological support=83.3 %; high RoB=83.3 %). In post-traumatic stress disorder (PTSD), MDMA reduced PTSD symptoms compared to any control ( k = 11; SMD=-0.85 [-1.09; -0.60]; I 2 =0 %; GRADE=low). In major depressive disorder (MDD), psilocybin/ayahuasca/LSD reduced depressive symptoms ( k = 8; SMD=-0.62 [-0.97; -0.28]; I 2 =55 %; GRADE=very low). In anxiety disorders, both MDMA and serotonergic psychedelics reduced anxiety symptoms (SMD MDMA =-1.18 [-2.04; -0.32]; I 2 =0 %; k = 2; GRADE=low and SMD serotonergic =-0.88 [-1.70; -0.06]; I 2 =54 %; k = 5; GRADE=very low). In alcohol use disorder, neither psilocybin nor LSD reduced abstinence rates ( k = 6; RR=1.42 [0.89; 2.26]; I 2 =7 %; GRADE=very low). In attention-deficit hyperactivity disorder (ADHD), LSD did not reduce ADHD symptoms ( k = 1; SMD=0.22 [-0.32; 0.76]; GRADE=very low). Moderate certainty in evidence was only found for MDMA on PTSD symptoms when compared to placebo. MDMA/serotonergic psychedelics were not associated with higher risk of all-cause discontinuation (RR MDMA =0.74 [0.32; 1.72]; RR serotonergic =0.81 [0.56; 1.15]). Overall, MDMA/serotonergic psychedelics are promising for the treatment of PTSD, MDD, and anxiety disorders with moderate to large effect sizes. Pragmatic trials, long-term, head-to-head trials exploring the role of psychological support, aiming to identify predictors of response, and accounting for expectancy and functional unblinding are needed. Studies addressing these limitations will likely be required for regulatory approval of psychedelic drugs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.532
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.081
GPT teacher head0.411
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2025
Admission routes2
Has abstractyes

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