Combination analgesic development for enhanced clinical efficacy (the CADENCE trial): a double-blind, controlled trial of an alpha-lipoic acid–pregabalin combination for fibromyalgia pain
Bibliographic record
Abstract
ABSTRACT: Drug therapy for fibromyalgia is limited by incomplete efficacy and dose-limiting adverse effects (AEs). Combining agents with complementary analgesic mechanisms-and differing AE profiles-could provide added benefits. We assessed an alpha-lipoic acid (ALA)-pregabalin combination with a randomized, double-blind, 3-period crossover design. Participants received maximally tolerated doses of ALA, pregabalin, and ALA-pregabalin combination for 6 weeks. The primary outcome was daily pain (0-10); secondary outcomes included Fibromyalgia Impact Questionnaire, SF-36 survey, Medical Outcomes Study Sleep Scale, Beck Depression Inventory (BDI-II), adverse events, and other measures. The primary outcome of daily pain (0-10) during ALA (4.9), pregabalin (4.6), and combination (4.5) was not significantly different ( P = 0.54). There were no significant differences between combination and each monotherapy for any secondary outcomes, although combination and pregabalin were both superior to ALA for measures of mood and sleep. Alpha-lipoic acid and pregabalin maximal tolerated doses were similar during combination and monotherapy, and AEs were not frequent with combination therapy. These results do not support any additive benefit of combining ALA with pregabalin for fibromyalgia. The observation of similarly reached maximal tolerated drug doses of these 2 agents (which have differing side-effect profiles) during combination and monotherapy-without increased side effects-provides support for future development of potentially more beneficial combinations with complementary mechanisms and nonoverlapping side effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".