305 Pregabalin has efficacy for hand osteoarthritis pain: a proof-of-concept study using pain sensitisation measures
Bibliographic record
Abstract
Background: Osteoarthritis (OA) is the most prevalent arthritis worldwide and is characterised by chronic pain and impaired physical function. Recent reports suggest that OA pain has inflammatory and neuropathic pain components, with many patients experiencing ongoing pain even after receiving treatment according to NICE guidelines including paracetamol, non-steroidal anti-inflammatory drugs (NSAIDs) and joint injections. We have previously demonstrated that people with hand OA report features of pain sensitisation. We therefore hypothesised that heightened pain in hand OA could be reduced with duloxetine or pregabalin. In this prospective, double-blind, randomised clinical study, we recruited 65 participants, aged 40-75 years, with a Numerical Rating Scale (NRS) for pain of at least 5. Methods: Participants were randomised to one of the following three groups: duloxetine, pregabalin, and placebo. The primary endpoint was the NRS pain score, and the secondary endpoints included the Australian and Canadian Hand Osteoarthritis Index (AUSCAN) pain, stiffness, and function scores and quantitative sensory testing by pain pressure algometry. Participants were recruited from St George's University Hospitals NHS Trust and participation identification centres across the South London region. Each randomised subject was in the trial for a total of 13 weeks. All data was collected prospectively throughout the trial.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".