Treatment approaches and efficacy in psychedelic-induced psychosis: A systematic review
Bibliographic record
Abstract
Psychedelics are increasingly used in the general population, yet they are associated with increased risk of psychosis in a minority of users that can experience psychedelic-induced psychosis (<1 % in controlled trial settings). In contrast, the evidence regarding the treatment of psychedelics-induced psychosis remains to date scarce. We conducted a PRISMA 2020-compliant systematic review (CRD42023399591), searching electronic databases (inception-August 2024) for interventional, observational studies, case series, or case reports on the treatment of psychedelic-induced psychosis. Frequencies of population, treatment, and outcome characteristics were analyzed. We included 14 case series, 20 case reports, and one prospective study, reporting on 93 cases of psychedelic-induced psychosis, between 1955 and 2024. The primary substances implicated were LSD (47.3 %) and MDMA (38.7 %), and the average patient age of 23.7 ± 6.3 years, with a predominance of male subjects (88 %). Psychosis lasted an average of 1.8 weeks. We identified two main treatment categories: first-generation antipsychotics (n = 37) and second-generation antipsychotics (n = 57). Electroconvulsive therapy was used in a minor subset of cases (n = 9). The response rate for first-generation antipsychotics (27 %) was significantly lower than that for second-generation agents (91.3 %) and electroconvulsive therapy (91 %). Follow-up data indicated 34 % of patients later developed schizophrenia spectrum disorders, and 20.4 % were diagnosed with bipolar disorder. However, the lack of comprehensive follow-up limits the interpretation of findings In conclusion, the evidence supporting treatment options remains limited, primarily based on case reports. Our findings suggest that second-generation antipsychotics seem to be more beneficial in managing psychedelic-induced psychosis, warranting further investigation into optimized treatment protocols.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".