Impaired awareness: Why people with multiple sclerosis continue using cannabis despite evidence to the contrary
Bibliographic record
Abstract
BACKGROUND: With widespread moves toward legalization of cannabis, increasing numbers of people with multiple sclerosis (pwMS) are using the drug. Emerging MS-related data show that cannabis can cause or exacerbate cognitive dysfunction. OBJECTIVE: To understand why people with MS continue using cannabis despite adverse cognitive consequences. It was hypothesized that lack of awareness, a component of metacognition, could explain this decision, in part. METHOD: Forty pwMS who smoked cannabis almost daily were assigned by odd-even case number selection to either a cannabis continuation (CC) or cannabis withdrawal (CW) group. Both groups were followed for 28 days. All participants completed, at baseline and day 28, the brief repeatable battery of neuropsychological tests (BRNB) in MS for measures of processing speed, memory and executive function; Modified fatigue impact scale (mFIS) for self-report indices of cognitive functioning. RESULTS: No significant baseline differences between the groups on the BRNB and mFIS. At day 28, significant improvement within group was seen on all measures of the BRNB, but only in the CW group (p = .0001 for all indices). A repeat measure ANOVA did not find any significant group (CC vs. CW) × time (baseline and day 28) interactions for the self-report cognitive measures on the mFIS. Cannabis abstainers did report less ability to function away from home. All 19 participants in the CW group reverted to using cannabis on study completion despite being informed individually of their cognitive improvement. CONCLUSIONS AND RELEVANCE: The inability of pwMS to accurately appraise their memory and executive function can help explain, in part, why they continue to smoke cannabis despite objective evidence of the deleterious cognitive side effects of this behavior.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".