Evaluating the impact of gabapentinoids on sleep health in patients with chronic neuropathic pain: a systematic review and meta-analysis
Bibliographic record
Abstract
Chronic neuropathic pain (NP) is debilitating and impacts sleep health and quality of life. Treatment with gabapentinoids (GBs) has been shown to reduce pain, but its effects on sleep health have not been systematically evaluated. The objective of this systematic review and meta-analysis was to assess the relationship between GB therapy dose and duration on sleep quality, daytime somnolence, and intensity of pain in patients with NP. Subgroup comparisons were planned for high- vs low-dose GBs, where 300 mg per day or more of pregabalin was used to classify high-dose therapy. Trial data were segregated by duration less than 6 weeks and 6 weeks or greater. Twenty randomized controlled trials were included. Primary outcome measures included pain-related sleep interference and incidence of daytime somnolence. Secondary outcomes included daily pain scores (numerical rating scale 0-10) and patient global impression of change. Significant improvement in sleep quality was observed after 6 weeks of GB treatment when compared with placebo (standardized mean difference 0.39, 95% confidence interval 0.32-0.46 P < 0.001). Increased daytime somnolence was observed among all GB-treated groups when compared with placebo. Treated patients were also more likely to report improvement of patient global impression of change scores. Pain scores decreased significantly in patients both after 6 weeks of treatment (P < 0.001) and in trials less than 6 weeks (P = 0.017) when compared with placebo. Our data demonstrate that GBs have a positive impact on sleep health, quality of life, and pain in patients with NP syndromes. However, these benefits come at the expense of daytime somnolence.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".