Duloxetine in OsteoArthritis (DOA) study: effects of duloxetine on pain and function in end-stage hip and knee OA – a pragmatic enriched randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Some osteoarthritis (OA) patients experience inadequate pain relief from analgesics like acetaminophen and nonsteroidal anti-inflammatory drugs. This could be the result of experienced non-nociceptive centralized pain. Placebo-controlled randomized trials (RCT) have proven the effectiveness of duloxetine for OA and several chronic pain conditions where central sensitization (CS) is one of the key underlying pain mechanisms. OBJECTIVES: Assess the efficacy of an 8-week duloxetine treatment compared to usual care in end-stage knee and hip OA patients with a level of centralized pain. DESIGN: Pragmatic, enriched, open-label RCT. METHODS: Patients were randomized to duloxetine or to care-as-usual. Primary outcome was pain in the index joint, measured with the pain domain of the Knee injury and Osteoarthritis Outcome Score (KOOS) or the Hip disability and Osteoarthritis Outcome Score (HOOS). The intention-to-treat principle was used, with mixed-model repeated measures to analyze the effect. RESULTS: One hundred eleven patients were randomized. Nearly 44% felt much to very much better after duloxetine usage compared to 0% in the care-as-usual group (p < 0.001). The duloxetine group scored 11.3 points (95%CI: 5.8, 16.8) better on the pain domain of the KOOS/HOOS (p < 0.001). Knee patients improved significantly more than hip patients (18.7 [95%CI: 11.3, 26.1] versus 6.0 [95%CI: - 2.6, 14.5] points better). CONCLUSIONS: Adding duloxetine treatment seems to be beneficial for end-stage knee OA patients with neuropathic-like symptoms (at risk of CS). End stage Hip OA patients seem to be nonresponsive to duloxetine. TRIAL REGISTRATION: Dutch Trial Registry with number NTR 4744 (15/08/2014) and in the EudraCT database with number 2013-004313-41 .
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".