Combination pharmacotherapy for the treatment of neuropathic pain in adults: systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT: Neuropathic pain causes substantial morbidity and healthcare utilization. Monotherapy with antidepressants or anticonvulsants often fails to provide relief. Combining different drugs sometimes provides improved analgesia and/or tolerability. More than half of patients receive 2 or more analgesics, and combination trials continue to emerge. This review comprehensively searched CENTRAL, MEDLINE, and EMBASE for relevant trials. Included studies are double-blind randomized controlled trials evaluating combinations of 2 or more drugs vs placebo or at least one monotherapy in adults with neuropathic pain. Outcomes included measures of efficacy and adverse effects. Risk of bias was assessed. Meta-analyses compared combination to monotherapy wherever 2 or more similar studies were available. Forty studies (4741 participants) were included. Studies were heterogenous with respect to various characteristics, including dose titration methods and administration (ie, simultaneous vs sequential) of the combination. Few combinations involved a nonsedating drug, and several methodological problems were identified. For opioid-antidepressant, opioid-gabapentinoid, and gabapentinoid-antidepressant combinations, meta-analyses failed to demonstrate superiority over both monotherapies. In general, adverse event profiles were not substantially different for combination therapy compared with monotherapy. Despite widespread use and a growing number of trials, convincing evidence has not yet emerged to suggest superiority of any combination over its respective monotherapies. Therefore, implementing combination therapy-as second- or third-line treatment-in situations where monotherapy is insufficient, should involve closely monitored individual dosing trials to confirm safety and overall added benefit. Further research is needed, including trials of combinations involving nonsedating agents, and to identify clinical settings and specific combinations that safely provide added benefit.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".