Randomized, double-blind, controlled trial of a combination of alpha-lipoic acid and pregabalin for neuropathic pain: the PAIN-CARE trial
Bibliographic record
Abstract
ABSTRACT: We compared a combination of the nonsedating antioxidant, alpha-lipoic acid (ALA), with the sedating anticonvulsant, pregabalin, vs each monotherapy to treat neuropathic pain due to peripheral neuropathies. In this randomized, double-blind, 3-period crossover trial, participants received oral ALA, pregabalin, and their combination-each for 6 weeks. The primary outcome was mean daily pain intensity at maximal tolerated doses (MTD); secondary outcomes included quality of life (SF-36), sleep (Medical Outcomes Study-Sleep Scale), adverse effects, drug doses, and other measures. Of 55 participants randomized (20-diabetic neuropathy, 19-small fiber neuropathy, and 16-other neuropathies), 46 completed 2 periods, and 44 completed 3. At MTD, the primary outcome of mean pain intensity (0-10) was 5.32 (standard error, SE = 0.18), 3.96 (0.25), 3.25 (0.25), and 3.16 (0.25) at baseline, ALA, pregabalin, and combination, respectively ( P < 0.01 for ALA vs combination and pregabalin). Treatment differences were similar in subgroups with diabetic neuropathy and with other neuropathies. SF-36 total scores (higher number indicates better quality of life) were 66.6 (1.88), 70.1 (1.88), and 69.4 (1.87) with ALA, pregabalin, and combination ( P < 0.05 for ALA vs combination and pregabalin). At MTD, there were no statistically significant treatment differences in adverse effects or drug doses. This trial demonstrates superiority of pregabalin vs ALA but provides no evidence to suggest added benefit of combining ALA with pregabalin to treat neuropathic pain.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".