Gabapentin for Neuropathic Pain: Systematic Review of Controlled and Uncontrolled Literature
Bibliographic record
Abstract
OBJECTIVE: To assess the efficacy/effectiveness and side effects of gabapentin for the treatment of neuropathic pain. DESIGN: Systematic review of the literature. METHODS: Extensive search of several electronic databases located both controlled and uncontrolled studies. Efficacy was assessed through meta-analysis of randomized controlled trials (RCTs), whereas the effectiveness of gabapentin in uncontrolled studies was assessed via a novel system of dichotomous classification of "bad" versus "good" results. FINDINGS: Thirty-five papers involving 727 patients with multiple neuropathic pain conditions met the inclusion criteria. The meta-analysis of the 2 high-quality, placebo-controlled RCTs showed positive effect of gabapentin in diabetic neuropathy and post-herpetic neuralgia. The addition of 2 low-quality, placebo-controlled RCTs did not alter the magnitude or direction of observed effect. The uncontrolled studies demonstrated positive effect on pain in different neuropathic syndromes, as well as benefit on different types of neuropathic pain; highest dose administered and rate-of-dose escalation showed wide variability between prescribers. Fewer and less severe side effects were reported in the uncontrolled studies. CONCLUSIONS: Gabapentin seems to be effective in multiple painful neuropathic conditions. The variable prescribing patterns of the uncontrolled studies raise the suspicion that effectiveness may be reduced if one limits administration of the drug to very low doses, whereas rapid dose escalation may be associated with increased central nervous system side effects. Well-designed controlled trials may provide insight into differential symptom sensitivity to the drug.
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".