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Record W2623186536 · doi:10.1177/2055217317713027

Cannabis and cognitive functioning in multiple sclerosis: The role of gender

2017· article· en· W2623186536 on OpenAlexaff
Viral Patel, Anthony Feinstein

Bibliographic record

VenueMultiple Sclerosis Journal - Experimental Translational and Clinical · 2017
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersMultiple Sclerosis Society
KeywordsCannabisPsychologyCognitionRecallCalifornia Verbal Learning TestEffects of cannabisEffects of sleep deprivation on cognitive performanceVerbal memoryVerbal learningClinical psychologyPsychiatryDevelopmental psychologyAudiologyMedicineCannabidiol

Abstract

fetched live from OpenAlex

Background Cognitive function in people with multiple sclerosis (PwMS) is associated with gender differences and the use of smoked/ingested cannabis. Objective The objective of this report is to explore a possible gender-cannabis interaction associated with cognitive dysfunction in PwMS. Methods A retrospective analysis was undertaken of cognitive data collected from 140 PwMS. A general linear model was conducted to determine gender and cannabis effects on processing speed (SDMT), verbal (CVLT-II) and visual (BVMT-R) memory, and executive functions (D-KEFS), while controlling for age and years of education. Results Cannabis was smoked at least once a month by 33 (23.6%) participants. Cannabis users were more impaired on the SDMT ( p = 0.044). Men, who comprised 30.7% of the entire sample and 42.2% of cannabis users, were more impaired on the CVLT-II (total learning, p = 0.001; delayed recall, p = 0.004). A cannabis-gender interaction was found with the CVLT-II delayed recall ( p = 0.049) and BVMT-R total learning ( p = 0.014), where male cannabis users performed more poorly than female. Conclusion Males with MS may be particularly vulnerable to the cognitive side effects of smoked cannabis use.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.059
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.168
GPT teacher head0.369
Teacher spread0.201 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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