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Record W2281587378 · doi:10.1177/1352458515607652

What to make of cannabis and cognition in MS: In search of clarity amidst the haze

2015· review· en· W2281587378 on OpenAlexaff
Anthony Feinstein, Emma Banwell, Bennis Pavisian

Bibliographic record

VenueMultiple Sclerosis Journal · 2015
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsCannabisCognitionMultiple sclerosisEffects of cannabisPsychologyCLARITYCognitive psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Given data showing that cannabis (herbal drug from the Cannabis sativa plant) can impair cognition in healthy subjects, the possibility that it may also do so in people with multiple sclerosis (MS) should be cause for concern. Approximately 20% of people with MS inhale or ingest cannabis for a variety of symptoms, or as a lifestyle choice. In addition, pharmaceutically manufactured cannabis (in capsules or spray) is prescribed most often for pain and spasticity; however, there is a dearth of literature on the cognitive effects of cannabis. Furthermore, methodological limitations introduce a cautionary note when interpreting the data. The evidence, which must therefore be considered preliminary, suggests that smoked cannabis may further compromise information processing speed and memory, with magnetic resonance imaging (fMRI) demonstrating more inefficient patterns of cerebral activation during task performance. The findings related to pharmaceutically manufactured cannabis are equivocal. There is a pressing need for further research to inform clinical opinion, which at present reflects a combination of uncertainty and dogma.

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)0.004
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.179
GPT teacher head0.381
Teacher spread0.202 · 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 designOther design
Domainnot available
GenreReview

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

Citations17
Published2015
Admission routes1
Has abstractyes

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