Controlled-release oxycodone relieves neuropathic pain: a randomized controlled trial in painful diabetic neuropathy
Bibliographic record
Abstract
BACKGROUND: Painful neuropathy is one of the most common long-term complications of diabetes mellitus and often proves difficult to relieve. METHODS: Patients with diabetic neuropathy with moderate or greater pain for at least 3 months, were evaluated for efficacy, safety and health-related quality of life (QOL) while receiving controlled-release (CR) oxycodone (OxyContin) or active placebo. Patients underwent washout from all opioids 2-7 days before randomization to 10 mg CR oxycodone or active placebo (0.25 mg benztropine) q12h. The dose was increased, approximately weekly, to a maximum of 40 mg q12h CR oxycodone or 1 mg q12h benztropine, with crossover to the alternate treatment after a maximum of 4 weeks. Acetaminophen, 325-650 mg q4-6h prn was provided as rescue. RESULTS: Thirty-six patients were evaluable for efficacy (21 men, 15 women, mean age 63.0+/-9.4 years). CR oxycodone resulted in significantly lower (P=0.0001) mean daily pain (21.8+/-20.7 vs. 48.6+/-26.6 mm VAS), steady pain (23.5+/-23.0 vs. 47.6+/-30.7 mm VAS), brief pain (21.8+/-23.5 vs. 46.7+/-30.8 mm VAS), skin pain (14.3+/-20.4 vs. 43.2+/-31.3 mm VAS), and total pain and disability (16.8+/-15.6 vs. 25.2+/-16.7; P=0.004). Scores from 6 of the 8 SF-36 domains and both summary scales, Standardized Physical Component (P=0.0002) and Standardized Mental Component (P=0.0338) were significantly better during CR oxycodone treatment. The number needed to treat to obtain one patient with at least 50% pain relief is 2.6 and clinical effectiveness scores favoured treatment with CR oxycodone over placebo (P=0.0001). CONCLUSION: CR oxycodone is effective and safe for the management of painful diabetic neuropathy and improves QOL.
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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) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".