Pregabalin for the Treatment of Neuropathic Pain: A Systematic Review of Patient-Reported Outcomes
Bibliographic record
Abstract
Neuropathic pain (NeP) arises from pathologies of the nervous system, significantly impacting patient functionality and quality of life. Pregabalin is a first-line treatment for NeP, but there has been limited focus on patient-reported outcomes (PROs). This systematic review synthesizes PROs from clinical studies assessing pregabalin's efficacy in treating NeP, with a focus on sleep disturbance, mental health, and health-related quality of life (HRQOL). Following Cochrane Collaboration guidelines, we conducted a systematic search of randomized controlled trials and epidemiological studies reporting PROs associated with pregabalin treatment for NeP. Sixteen studies met the inclusion criteria and were narratively synthesized. The findings indicate that pregabalin significantly improved sleep interference and HRQOL across multiple studies, particularly at doses of 300 mg/day or higher. However, improvements in mental health outcomes, such as anxiety and depression, were inconsistent across studies. No meta-analysis was conducted due to the heterogeneity of outcomes. In conclusion, while pregabalin shows robust efficacy in reducing NeP-related sleep disturbances, its effects on mental health and HRQOL are less consistent. These findings highlight the need for a more holistic approach to NeP treatment, incorporating both clinical outcomes and PROs.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".