Efficacy of cannabinoids compared to the current standard treatments on symptom relief in persons with multiple sclerosis (CANSEP trial): study protocol for a randomized clinical trial
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) is an inflammatory and degenerative disease of the central nervous system. More than 90,000 Canadians are affected; a cure is yet to be found. Available treatments to manage the disease course are only partially effective. For many years, persons with MS (PwMS) have used cannabis to relax, to reduce pain and spasticity, or to improve sleep and daily functioning, despite the lack of scientific evidence on the efficacy of specific cannabinoids [i.e., tetrahydrocannabinol (THC) and cannabidiol (CBD)] on these MS symptoms. The purpose of this clinical trial is to assess the effectiveness of different doses of these cannabinoids, alone or combined, on spasticity relief, compared to placebo. Moreover, we aim to determine which treatment is best effective to address other key MS conditions. Methods: A double-blinded, randomized, factorial, placebo-controlled trial will be performed. We intend to include up to 250 PwMS aged over 21 recruited from the Centre hospitalier de l'Université de Montréal MS Clinic. PwMS will be randomly assigned on a 1:1:1:1 ratio to one of the trial arms: THC alone, CBD alone, THC/CBD combination, or placebo, using stratified blocked randomization, with random blocks within each stratum. The primary outcome is a self-assessment of spasticity using the mean Numeric Rating Scale score over 7 days. The main outcome will be the difference in this score at 4 weeks compared to baseline. Secondary outcomes include assessments of spasticity as measured by a clinician, pain, fatigue, sleep, bowel, bladder, and sexual dysfunction, restless legs syndrome, mental health, quality of life, mobility, cognitive functioning, and adverse events. Treatment responders are eligible for a 12-week extension phase, using the same treatment allocation and assessments. Discussion: Previous clinical studies examined the efficacy of cannabis-based medicines in PwMS, mostly using products with 1:1 THC/CBD ratio. The major barrier to effectively use cannabis in real-world clinical settings is the lack of evidence on benefits of specific cannabinoids and information on possible related risks. The CANSEP study will contribute to overcome these limitations and identify the risks and benefits of cannabis-based treatments in PwMS. Clinical trial registration: ClinicalTrials.Gov, NCT05092191.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.011 |
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".