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Record W2965029889 · doi:10.1093/brain/awz213

Coming off cannabis: a cognitive and magnetic resonance imaging study in patients with multiple sclerosis

2019· article· en· W2965029889 on OpenAlexafffund
Anthony Feinstein, Cecilia Meza, Cristiana Stefan, Richard W. Staines

Bibliographic record

VenueBrain · 2019
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of WaterlooUniversity of TorontoCentre for Addiction and Mental HealthSunnybrook Health Science Centre
FundersSanofi GenzymeMultiple Sclerosis Society of CanadaUniversity of TorontoTeva Pharmaceutical Industries
KeywordsCannabisAbstinenceMultiple sclerosisCognitionPsychologyPaced Auditory Serial Addition TestNeuropsychologyAudiologyCognitive testEffects of sleep deprivation on cognitive performanceEffects of cannabisMedicinePsychiatryCannabidiol

Abstract

fetched live from OpenAlex

Cognitive dysfunction affects 40-80% of patients with multiple sclerosis. Smoking cannabis may add to these deficits. It is unclear whether coming off cannabis results in cognitive improvement. To address this question, 40 patients with multiple sclerosis who started using cannabis after the onset of multiple sclerosis and who used it for at least 4 days a week over many years were divided by odd-even number selection into two groups: cannabis continuation and cannabis withdrawal. Assessments took place at baseline and after 28 days and included serial versions of the Brief Repeatable Neuropsychological Battery for multiple sclerosis containing tests of verbal and visual memory, processing speed and executive function; structural and functional MRI, the latter entailing a compatible version of the Symbol Digit Modalities Test; urine for cannabinoid metabolites to detect compliance with abstinence. Only those participants deemed globally impaired at baseline (failure on at least two cognitive domains) were enrolled. The results revealed that the two groups were well matched demographically and neurologically. One subject was removed from the withdrawal group because of failed abstinence. Urine analysis revealed the cannabinoid consumed was predominantly tetrahydrocannabinol (THC). There were no baseline between group cognitive differences, but by Day 28 the withdrawal group performed significantly better on every cognitive index (P < 0.0001 for all). Significant within group differences were present for every test over time, but only in the abstinent group (P < 0.0001 for all tests). There were no between group baseline or Day 28 differences in structural MRI indices (global atrophy, total T1 and T2 lesion volume). At index assessment the two groups had a similar performance on the functional MRI-compatible Symbol Digit Modalities Test and there were no group differences in brain activation. However, by Day 28, the withdrawal group completed more trials correctly (P < 0.012) and had a faster reaction time (P < 0.002), associated with significantly increased activation in brain regions known to be associated with performance of the test (bilateral inferior frontal gyri, caudate and declive/cerebellum, P < 0.001 for all regions). These results reveal that patients with multiple sclerosis who are frequent, long-term cannabis users can show significant improvements in memory, processing speed and executive function after 28 days of drug abstinence. The absence of similar improvements in a matched multiple sclerosis group that remained on cannabis shows that beneficial cognitive change after stopping cannabis is not solely attributable to the effects of practice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.081
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.272
Teacher spread0.248 · 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 teacher head, 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

Citations31
Published2019
Admission routes2
Has abstractyes

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