Ketamine Assisted Psychotherapy: A Systematic Narrative Review of the Literature
Bibliographic record
Abstract
Currently, ketamine is used in treating multiple pain, mental health, and substance abuse disorders due to rapid-acting analgesic and antidepressant effects. Its limited short-term durability has motivated research into the potential synergistic actions between ketamine and psychotherapy to sustain benefits. This systematic review on ketamine-assisted psychotherapy (KAP) summarizes existing evidence regarding present-day practices. Through rigorous review, seventeen articles that included 603 participants were identified. From available KAP publications, it is apparent that combined treatments can, in specific circumstances, initiate and prolong clinically significant reductions in pain, anxiety, and depressive symptoms, while encouraging rapport and treatment engagement, and promoting abstinence in patients addicted to other substances. Despite much variance in how KAP is applied (route of ketamine administration, ketamine dosage/frequency, psychotherapy modality, overall treatment length), these findings suggest psychotherapy, provided before, during, and following ketamine sessions, can maximize and prolong benefits. Additional large-scale randomized control trials are warranted to understand better the mutually influential relationships between psychotherapy and ketamine in optimizing responsiveness and sustaining long-term benefits in patients with chronic pain. Such investigations will assist in developing standardized practices and maintenance programs.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".