Effect of Milnacipran on Pain in Patients with Rheumatoid Arthritis with Widespread Pain: A Randomized Blinded Crossover Trial
Bibliographic record
Abstract
OBJECTIVE: Clinical trials have shown that serotonin norepinephrine reuptake inhibitors, such as milnacipran, decrease pain in noninflammatory pain conditions such as fibromyalgia and osteoarthritis. We examined the effect of milnacipran on self-reported pain intensity and experimental pain sensitivity among patients with rheumatoid arthritis (RA) with widespread pain and stable RA disease activity. METHODS: In this double-blind, crossover study, patients with RA with widespread pain, receiving a stable treatment regimen, were randomized (by a random number generator) to receive milnacipran 50 mg twice daily or placebo for 6 weeks, followed by a 3-week washout and crossed over to the other arm for the remaining 6 weeks. The primary outcome was change in average pain intensity, assessed by the Brief Pain Inventory short form. The sample size was calculated to detect a 30% improvement in pain with power = 0.80 and α = 0.05. RESULTS: Of the 43 randomized subjects, 41 received the study drug, and 32 completed the 15-week study per protocol. On a 0-10 scale, average pain intensity decreased by 0.39 (95% CI -1.27 to 0.49, p = 0.37) more points during 6 weeks of milnacipran treatment compared with placebo. In the subgroup of subjects with swollen joint count ≤ 1, average pain intensity decreased by 1.14 more points during 6 weeks of milnacipran compared with placebo (95% CI -2.26 to -0.01, p = 0.04). Common adverse events included nausea (26.8%) and loss of appetite (9.7%). CONCLUSION: Compared with placebo, milnacipran did not improve overall, self-reported pain intensity among subjects with widespread pain receiving stable RA medications. TRIAL REGISTRATION: ClinicalTrials.gov NCT01207453.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".