Cannabis and Cognitive Function in Multiple Sclerosis: Findings From a Large Consecutive Clinical Sample
Bibliographic record
Abstract
BACKGROUND: Cannabis is commonly used in persons with MS (pwMS) for symptom relief. Previous studies investigating the effects of cannabis on objective cognitive functioning in MS have yielded mixed results. AIM: To examine associations of recreational cannabis use with objective cognitive performance in a large, consecutive clinical sample of pwMS. METHODS: In a cross-sectional study, 847 pwMS provided information on cannabis consumption and were administered the Minimal Assessment of Cognitive Function in MS (MACFIMS) battery, assessing memory, executive function, language, and processing speed. Cognitive performance of cannabis users (N = 254) and nonusers (N = 593) was compared by means of a one-way MANOVA. Multiple linear regressions then examined whether cannabis use independently predicted cognitive test results. RESULTS: A significant effect of cannabis use on cognitive performance was observed, F(11, 692) = 1.955, p = 0.030, Wilks λ = 0.970, with users performing significantly poorer than nonusers on the SDMT [F(1, 703) = 6.099, p = 0.014]. Regression models, taking into account covariates (age, education, disease duration, anxiety, and fatigue) revealed that cannabis independently predicted performance on the SDMT, PASAT-3, and PASAT-2, all p < 0.001. Models explained 15.8%, 7.1%, and 8.7% of variance, respectively. CONCLUSION: This cross-sectional study provides evidence that cannabis use is associated with poorer processing speed and working memory in pwMS, thereby presenting a possible additional risk factor for cognitive impairment. This finding takes on added concern in light of cannabis use increasing with its legalization, and the worldwide increase of cannabis potency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".