Efficacy of ketamine in relieving neuropathic pain: a systematic review and meta-analysis of animal studies
Bibliographic record
Abstract
ABSTRACT: In humans, proof of long-term efficacy of ketamine treatment in neuropathic pain is lacking. To improve our understanding of ketamine behavior under various administration conditions, we performed a systematic review and meta-analyses of controlled studies on the efficacy of ketamine in mice and rats with a disease model of nerve injury on relief of allodynia. Searches in PubMed and EMBASE identified 31 unique studies. Four meta-analyses were conducted. The first analysis included 19 comparisons on a single ketamine dose and measurement of effect within 3 hours of dosing and showed an appreciable effect (standardized mean difference 1.6, 95% confidence interval 1.1-2.1). Subgroup analyses showed no effect of species, administration route, or dose. A single administration was insufficient to sustain relief of allodynia at 24 or 72 hours after dosing, as observed in our second analysis (7 comparisons) with similar effects in ketamine-treated and control animals. Chronic ketamine administration (9 comparisons) caused profound relief of allodynia when tested during ketamine exposure (effect size 5.1, 3.7-6.5). The final analysis (6 comparisons) showed that chronic administration caused a slow loss of relief of allodynia with 70% loss of effect 24 days after end of treatment. No subgroups analyses were possible in the last 3 meta-analyses due to small group sizes. These results indicate long-term ketamine anti-allodynic effects after chronic exposure (>3 days) but not after a single administration. Given several limitations, extrapolation of the animal data to the human condition is tenuous.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".