Efficacy and safety of racemic ketamine and esketamine for depression: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Racemic ketamine and esketamine have demonstrated rapid antidepressant effects. We aimed to review the efficacy and safety of racemic and esketamine for depression. RESEARCH DESIGN AND METHODS: We conducted a PRISMA-guided review for relevant randomized controlled trials of racemic or esketamine for unipolar or bipolar major depression from database inception through 2021. We conducted random-effects meta-analyses using pooled rate ratios (RRs) and Cohen's standardized mean differences (d) with their 95% confidence intervals (CI). RESULTS: We found 36 studies (2903 participants, 57% female, 45.1 +/- 7.0 years). Nine trials used esketamine, while the rest used racemic ketamine. The overall study quality was high. Treatment with any form of ketamine was associated with improved response (RR=2.14; 95% CI, 1.72-2.66; I2=65%), remission (RR=1.64; 95% CI, 1.33-2.02; I2=39%), and depression severity (d=-0.63; 95% CI, -0.80 to -0.45; I2=78%) against placebo. Overall, there was no association between treatment with any form of ketamine and retention in treatment (RR=1.00; 95% CI, 0.99-1.01; I2<1%), dropouts due to adverse events (RR=1.56; 95% CI, 1.00-2.45; I2<1%), or the overall number of adverse events reported per participant (OR=2.14; 95% CI, 0.82-5.60; I2=62%) against placebo. CONCLUSIONS: Ketamine and esketamine are effective, safe, and acceptable treatments for individuals living with depression.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".