Gabapentin and pregabalin for chronic neuropathic and early postsurgical pain: current evidence and future directions
Bibliographic record
Abstract
PURPOSE OF REVIEW: Gabapentin and pregabalin bind to the alpha-2-delta calcium channel subunit and represent a novel analgesic drug class. The evidence base supporting their use for chronic neuropathic and early postsurgical pain is reviewed. RECENT FINDINGS: Multiple, large, high-quality trials have demonstrated the safety and efficacy of gabapentin and pregabalin in neuropathic pain. Treatment-related improvement of pain and sleep positively impact upon quality of life. Sedation, dizziness and ataxia are important and relatively common adverse effects, however. Accumulating evidence indicates that gabapentin, and possibly pregabalin, also exert important effects following surgery. Multiple high-quality trials have demonstrated analgesic and opioid-sparing efficacy with gabapentin following various surgical procedures. Gabapentin and pregabalin reduce movement-evoked pain and this can lead to enhanced functional postoperative recovery. Postoperative opioid sparing is of questionable relevance since few trials have shown reduced opioid-related adverse effects. Sedation, dizziness and ataxia have been reported in only a few trials. Future larger-scale perioperative trials focused on safety assessment are needed, however. SUMMARY: Gabapentin and pregabalin are efficacious treatments for neuropathic and postsurgical pain. Future research addressing several specific questions would serve to better delineate their optimal roles in treating these and other pain conditions.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".