Ketamine's Altered States Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: In recent years, there has been a significant focus on exploring the potential therapeutic impact of altered states of consciousness on treatment outcomes for mental illness, with the goal of enhancing therapeutic strategies and patient results. METHODS: This meta-analysis was designed to investigate the potential link between the psychomimetic effects of ketamine and clinical outcomes in mental health, which adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: Eleven studies were selected for meta-analysis, and the main result did not find a significant correlation between the psychoactive effects of ketamine and clinical outcomes either in mental illness (n = 11; n's = 27; r = 0.06 [-0.05, 0.17]; P = 0.268) or depression exclusively (n = 10; n's = 25; r = 0.03 [-0.07, 0.13]; P = 0.561). High heterogeneity was found for general analysis ( I2 = 80.78). Egger's regression did not indicate publication bias (intercept = 1.57; SE = 1.49, P = 0.30). No significant Kendall's rank correlation coefficient was observed ( τ = 0.02, P = 0.88) indicating funnel plot symmetry. The sub-analyses, aimed at minimizing study variability by specifically examining factors such as patient disorders (limited to depression), methods of administration (exclusively intravenous), types of assessment instruments, and the timing of evaluations, also yielded no significant findings. CONCLUSION: This meta-analysis suggests that the altered states of consciousness experienced during ketamine sessions are not directly linked to clinical outcomes. However, it is important to acknowledge that the limited number of studies and their heterogeneity render this conclusion preliminary, warranting further investigation over time.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.070 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".