Psychedelics and Suicide-Related Outcomes: A Systematic Review
Bibliographic record
Abstract
Background/Objectives: Suicide accounts for 1.4% of global deaths, and the slow-acting nature of traditional treatments for suicide risk underscores the need for alternatives. Psychedelic therapies may rapidly reduce suicide risk. This systematic review evaluates impact of psychedelic therapies on suicide-related outcomes. Methods: A systematic search of MEDLINE, Embase, PsycINFO, and ClinicalTrials.gov was conducted up to November 2024. Results: Four randomized controlled trials (RCTs) evaluated suicidality as a secondary outcome or safety measure, showing significant reductions in suicidal ideation with psilocybin (three studies) and MDMA-assisted therapy (MDMA-AT; one study). Effect sizes, measured by Cohen’s d, ranged from =0.52 to 1.25 (p = 0.01 to 0.005), with no safety issues reported. Five additional RCTs assessed suicidality as a safety measure, showing reductions in suicidal ideation with psilocybin (two studies) and MDMA-AT (three studies; p = 0.02 to 0.04). Among 24 non-randomized and cross-sectional studies, results were mixed. Psilocybin (three studies) reduced suicidal ideation, with odds ratios (OR) of 0.40–0.75. MDMA-AT (five studies in PTSD patients) had a pooled effect size of d = 0.61 (95% CI: 0.32–0.89). LSD (six studies) showed increased odds of suicidality, with odds ratios ranging from 1.15 to 2.08. Studies involving DMT (two studies) and multiple psychedelics (three studies) showed mixed results, with DMT studies not showing significant effects on suicidality and studies involving multiple psychedelics showing varying outcomes, some reporting reductions in suicidal ideation and others showing no significant change. Conclusions: The effect of psychedelic therapies on suicide-related outcomes remains inconclusive, highlighting the need for further trials to clarify safety and therapeutic mechanisms.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".