Pregabalin relieves symptoms of painful diabetic neuropathy
Bibliographic record
Abstract
OBJECTIVE: Pregabalin, an alpha2-delta ligand with analgesic, anxiolytic, and anticonvulsant activity, has been evaluated for treatment of neuropathic pain. The authors assessed the efficacy and tolerability of pregabalin (75, 300, 600 mg/day) vs placebo in patients with diabetic peripheral neuropathy (DPN). METHODS: Patients with a 1- to 5-year history of DPN and average weekly pain score of > or =4 on an 11-point numeric pain-rating scale were enrolled in a 5-week, double-blind, multicenter, placebo-controlled study. Patients (n = 338) were randomized to receive one of three doses of pregabalin or placebo TID. Pregabalin 600 mg/day was titrated over 6 days; lower doses were initiated on day 1. RESULTS: Patients in the 300- and 600-mg/day pregabalin groups showed improvements in endpoint mean pain score (primary efficacy measure) vs placebo (p = 0.0001). Improvements were also seen in weekly pain score, sleep interference score, patient global impression of change, clinical global impression of change, SF-McGill Pain Questionnaire, and multiple domains of the SF-36 Health Survey. Improvements in pain and sleep were seen as early as week 1 and were sustained throughout the 5 weeks. Responders (patients with > or =50% reduction in pain compared to baseline) were 46% (300 mg/day), 48% (600 mg/day), and 18% (placebo). Pregabalin was well tolerated with a low discontinuation rate. The most common adverse events were dizziness and somnolence. CONCLUSIONS: In patients with diabetic peripheral neuropathy, pregabalin demonstrated early and sustained improvement in pain and a beneficial effect on sleep, which were confirmed by positive patient global impression. Pregabalin was well tolerated at all doses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".