A Survey of Cannabis Use in a Large US-Based Cohort of People with Multiple Sclerosis
Bibliographic record
Abstract
Abstract Background: As cannabis products become increasingly accessible across the United States, it is important to understand the contemporary use of cannabis for managing multiple sclerosis (MS) symptoms. Methods: We invited participants with MS from the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry (aged 18 years or older) to complete a supplemental survey on cannabis use between March and April 2020. Participants reported cannabis use, treated symptoms, patterns, preferences, methods of use, and the factors limiting use. Findings are reported using descriptive statistics. Results: Of the 6934 participants invited, 3249 responded. Of the respondents, 31% reported having ever used cannabis to treat MS symptoms, with 20% currently using cannabis. The remaining 69% had never used cannabis for MS symptoms, for reasons including not enough data about efficacy (40%) and safety (27%), and concerns about legality (25%) and cost (18%). The most common symptoms current users were attempting to treat were spasticity (80%), pain (69%), and sleep problems (61%). Ever users (vs never users) were more likely to be younger, be non-White, have lower education, reside in the Northeast and West, be unemployed, be younger at symptom onset, be currently smoking, and have higher levels of disability and MS-related symptoms (all P < .001). Conclusions: Despite concerns about insufficient safety and efficacy data, legality, and cost, almost one-third of NARCOMS Registry respondents report having tried nonprescription cannabis products in an attempt to alleviate their symptoms. Given the lack of efficacy and safety data on such products, future research in this area is warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".