A Randomized, Double-Blind, Placebo Controlled, Parallel Assignment, Flexible Dose, Efficacy Study of Nabilone as Adjuvant in the Treatment of Diabetic Peripheral Neuropathic Pain Using an Enriched Enrollment Randomized Withdrawal Design (S38.003)
Bibliographic record
Abstract
Objective: This study evaluated the oral cannabinoid, nabilone, in the treatment of refractory human diabetic peripheral neuropathic pain. Background Cannabinoids are effective for the reduction of neuropathic pain behaviours in animal models of neuropathic pain, but their efficacy in human patients is poorly established. Design/Methods: This was a single center, randomized, double blind, placebo controlled, flexible-dose study with an enriched enrollment randomized withdrawal design. Diabetic subjects with a pain score ≥4 (0–10 scale) continued their regular pain medications and were administered single blinded adjuvant nabilone for 4 weeks. Those subjects achieving ≥30% pain relief were then randomized and treated with either flexible-dose nabilone 1-4 mg/day (n = 13) or placebo (n = 13) in a further 5 week double blind treatment period. Results: Nabilone was associated with a significant improvement in the change in mean end-point pain score vs. placebo; mean treatment difference was a reduction of 1.27 (95% CI, 2.29 to 0.25) (p= 0.02). The average nabilone dose at end point was 2.9±1.1 mg/day. Nabilone was also associated with improvements from baseline when compared to placebo for the anxiety subscale of the Hospital Anxiety and Depression Scale, the Medical Outcomes Study sleep scale sleep problems index, and the European Quality of Life-5 Domains index score (each p Conclusions: Flexible-dose nabilone 1–4 mg/day was effective in relieving neuropathic pain, improvement of disturbed sleep and quality of life, and overall patient status. Nabilone was very well tolerated in patients with diabetic peripheral neuropathic pain. Larger scale studies are required to validate the use of nabilone in neuropathic pain management. Supported by: Unrestricted grant from Valeant Canada. Disclosure: Dr. Toth has received personal compensation for activities with Pfizer and Eli Lilly & Company as a speaker. Dr. Toth has received research support from Pfizer Inc, Valeant Pharmaceuticals International and Eli Lilly & Company. Dr. Mawani has nothing to disclose. Dr. Brady has nothing to disclose. Dr. Chan has nothing to disclose. Dr. Liu has nothing to disclose. Dr. Mehina has nothing to disclose. Dr. Garven has nothing to disclose. Dr. Bestard has nothing to disclose. Dr. Korngut has nothing to disclose.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".