Bibliographic record
Abstract
Background: A significant minority of patients with multiple sclerosis (MS) use cannabis, yet no study has examined the possible effects on mentation. Here, we report the emotional and cognitive correlates of street cannabis use in patients with MS. Methods: A sample of 140 consecutive patients with MS were interviewed with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) Axis I disorders (SCID-IV) from which details of cannabis use were recorded. Cognition was assessed using the Neuropsychological Battery for MS supplemented with the Symbol Digit Modalities Test (SDMT), an index of information processing speed, working memory, and sustained attention. Results: Ten subjects (7.7%) were defined as current cannabis users based on use within the last month. Compared to non-cannabis users (n = 130), they were younger (p = 0.001). Each of the 10 current cannabis users was matched on demographic and disease variables to four subjects with MS who did not use cannabis (total control sample n = 40). Group comparisons revealed that the proportion of patients meeting DSM-IV criteria for a psychiatric diagnosis was higher in cannabis users (p = 0.04). In addition, on the SDMT, cannabis users had a slower mean performance time (p = 0.006) and a different pattern of response compared to matched controls (group × time interaction; p = 0.001). Conclusions: Inhaled cannabis is associated with impaired mentation in patients with multiple sclerosis, particularly with respect to cognition. Future studies are required to clarify the direction of this relationship. GLOSSARY: 7/24 = 7/24 Spatial Learning Test; BSS = Beck Suicide Scale; COWAT = Controlled Oral Word Association Test; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders; EDSS = Expanded Disability Status Scale; HADS = Hospital Anxiety and Depression Scale; MS = multiple sclerosis; NPBMS = Neuropsychological Battery for MS; PASAT = Paced Auditory Serial Addition Task; SCID-IV = Structured Clinical Interview for DSM-IV Axis I disorders; SDMT = Symbol Digit Modalities Test; SRT = Selective Reminding Test; SSSI = Social Stress and Support Inventory.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".