Study protocol for a randomised, double-blind, placebo-controlled study evaluating the Efficacy of cannabis-based Medicine Extract in slowing the disease pRogression of Amyotrophic Lateral sclerosis or motor neurone Disease: the EMERALD trial
Bibliographic record
Abstract
INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder with no known cure and with an average life expectancy of 3-5 years post diagnosis. The use of complementary medicine such as medicinal cannabis in search for a potential treatment or cure is common in ALS. Preclinical studies have demonstrated the efficacy of cannabinoids in extending the survival and slowing of disease progression in animal models with ALS. There are anecdotal reports of cannabis slowing disease progression in persons with ALS (pALS) and that cannabis alleviated the symptoms of spasticity and pain. However, a clinical trial in pALS with these objectives has not been conducted. METHODS AND ANALYSIS: The Efficacy of cannabis-based Medicine Extract in slowing the disease pRogression of Amyotrophic Lateral sclerosis or motor neurone Disease trial is a randomised, double-blind, placebo-controlled cannabis trial in pALS conducted at the Gold Coast University Hospital, Australia. The investigational product will be a cannabis-based medicine extract (CBME) supplied by CannTrust Inc., Canada, with a high-cannabidiol-low-tetrahydrocannabinol concentration. A total of 30 pALS with probable or definite ALS diagnosis based on the El Escorial criteria, with a symptom duration of <2 years, age between 25 and 75years and with at least 70% forced vital capacity (FVC) will be treated for 6 months. The primary objective of the study is to evaluate the efficacy of CBME compared with placebo in slowing the disease progression measured by differences in mean ALS Functional Rating Scale-Revised and FVC score between the groups at the end of treatment. The secondary objectives are to evaluate the safety and tolerability of CBME by summarising adverse events, the effects of CBME on spasticity, pain, weight loss and quality of life assessed by the differences in mean Numeric Rating Scale for spasticity and Numeric Rating Scale for pain, percentage of total weight loss and ALS specific quality of life-Revised questionnaire. ETHICS AND DISSEMINATION: The study has been approved by the local Institutional Review Board. The results of this study will be published in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT03690791.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.157 | 0.029 |
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".