Nabiximols in the treatment of spasticity, pain and urinary symptoms due to multiple sclerosis
Bibliographic record
Abstract
INTRODUCTION: Over the last two decades, experimental and clinical data suggest a therapeutic benefit of cannabis-based medicines for a variety of multiple sclerosis (MS) symptoms. Clinical trials, both with synthetic or plant-derived cannabinoids, have demonstrated clinical efficacy of cannabinoids for the treatment of spasticity, neuropathic pain and bladder dysfunction. Nabiximols, a 1:1 mix of delta-9-tetrahydrocanabinol and cannabidiol extract from cloned chemovars, was licensed in the UK in 2010 and has also been approved in other European countries and Canada. The European Federation of Neurological Societies recommends that cannabis should be used only as a second or third line treatment in central neuropathic pain. AREAS COVERED: After a brief discussion of the endocannabinoid system, this review focuses on the use of cannabis to improve MS symptoms. More specifically, the authors have analyzed clinical studies on cannabis-based medicine extract (CBME), in particular nabiximols, in spasticity, as well as pain, and bladder dysfunction in MS. The authors have considered the large randomized controlled trials examining the psychological effects associated with cannabinoids use as well as long-term follow-up studies. EXPERT OPINION: Despite a number of trials with very promising results, there are still concerns related to relative paucity of data on long-term safety. Also, the long-term efficacy information in terms of the control of symptoms of a disease in which the natural history is progression is sparse. Therefore, further studies are required to improve the current knowledge of nabiximols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".