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Multiple sclerosis and cannabis

2008· article· en· W1994637767 on OpenAlexaff
Omar Ghaffar, Anthony Feinstein

Bibliographic record

VenueNeurology · 2008
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsCannabisNeuropsychologyMultiple sclerosisCognitionEffects of cannabisPsychologyPsychiatryMedicineClinical psychologyAudiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.039
GPT teacher head0.266
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations44
Published2008
Admission routes1
Has abstractyes

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