Decreases in Suicidality Following Psychedelic Therapy: A Meta-Analysis of Individual Patient Data Across Clinical Trials.
Bibliographic record
Abstract
Suicide is a global health concern, and innovative interventions that target suicidality are needed. While psychedelic therapy shows promise for a range of mental health concerns, including suicidality, not all psychedelic therapy trials have published their suicidality results and no meta-analysis has been published on the topic. Therefore, we completed the first meta-analysis of patient-level data on the effects of psychedelics on suicidality. We conducted a systematic search of MEDLINE, PsycINFO, and PubMed for all psychedelic therapy clinical trials (last search: November 5, 2020). We identified all psychedelic therapy trials that included a measure or measure-item that assesses suicidality. Suicidality data were requested from study authors and extracted using a data extraction form developed for this study. We identified 8, and successfully collected data from 7, relevant trials. Analysis of standardized mean differences (SMDs) indicated that, relative to baseline, psychedelic therapy was associated with large effect sizes for acute (80-240 min) and sustained (1 day, 1-8 weeks, and 3-4 months) decreases in suicidality (SMD range = -1.48 to -2.36; 95% CI range, -4.30 to 0.23). At 6 months, the effect size was medium (SMD = -0.65; 95% CI, -1.14 to -0.16). Reductions in suicidality were significant at all time points except for 7-8 weeks. Acute and post-acute elevations in suicidality were rare (6.5% and 3.0%, respectively). Limitations include heterogeneous samples and interventions, as well as limited sample size and number of studies. Results provide preliminary support for the safety of psychedelic therapy and its positive effect on suicidality. Controlled trials that specifically evaluate the effect of psychedelic therapy on suicidality may be warranted.
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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 | 0.032 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.047 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".