Combination pharmacotherapy for neuropathic pain: current evidence and future directions
Bibliographic record
Abstract
Current drugs reduce neuropathic pain and improve mood and quality of life. However, as single agents they are limited by incomplete efficacy and dose-limiting adverse effects. Recent experimental and clinical data support the potential of combination pharmacotherapy for neuropathic pain. Therapeutic benefits may include greater efficacy, lower doses and fewer adverse effects. Due to potential adverse, as well as beneficial, drug interactions, safety and efficacy of specific combinations must be empirically evaluated. Techniques such as isobolographic analysis, response-surface modeling and other model-free tests have been used in order to characterize analgesic interactions as antagonistic, additive or synergistic. Whether synergistic or not, a clinically useful combination could simply have additive or even subadditive analgesia, provided that there is less additivity for side effects. Despite widespread clinical use, there are surprisingly few published observations on combination therapy for neuropathic pain. This review discusses future directions and proposes research strategies aimed at bridging current knowledge gaps, including safety, compliance and cost-effectiveness; discovering optimal drug combinations and dose ratios; comparing concurrent with sequential combination therapy; and combining more than two drugs. Continued close integration of basic and clinical sciences is crucial in further harnessing the potential of combination pharmacotherapy in neuropathic pain.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".