Ketamine for the treatment of mental health and substance use disorders: comprehensive systematic review
Bibliographic record
Abstract
BACKGROUND: In the past two decades, subanaesthetic doses of ketamine have been demonstrated to have rapid and sustained antidepressant effects, and accumulating research has demonstrated ketamine's therapeutic effects for a range of psychiatric conditions. AIMS: In light of these findings surrounding ketamine's psychotherapeutic potential, we systematically review the extant evidence on ketamine's effects in treating mental health disorders. METHOD: The systematic review protocol was registered in PROSPERO (identifier CRD42019130636). Human studies investigating the therapeutic effects of ketamine in the treatment of mental health disorders were included. Because of the extensive research in depression, bipolar disorder and suicidal ideation, only systematic reviews and meta-analyses were included. We searched Medline and PsycINFO on 21 October 2020. Risk-of-bias analysis was assessed with the Cochrane Risk of Bias tools and A Measurement Tool to Assess Systematic Reviews (AMSTAR) Checklist. RESULTS: We included 83 published reports in the final review: 33 systematic reviews, 29 randomised controlled trials, two randomised trials without placebo, three non-randomised trials with controls, six open-label trials and ten retrospective reviews. The results were presented via narrative synthesis. CONCLUSIONS: Systematic reviews and meta-analyses provide support for robust, rapid and transient antidepressant and anti-suicidal effects of ketamine. Evidence for other indications is less robust, but suggests similarly positive and short-lived effects. The conclusions should be interpreted with caution because of the high risk of bias of included studies. Optimal dosing, modes of administration and the most effective forms of adjunctive psychotherapeutic support should be examined further.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".