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Record W2990547522

A Systematic Review Examining Cannabis Use for the Treatment of Multiple Sclerosis

2019· review· en· W2990547522 on OpenAlexaboutno aff
Natasha Jb Breward

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2019
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsMultiple sclerosisCannabisMedicineData sciencePsychologyPsychiatryComputer science
DOInot available

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) is a neurodegenerative disease that affects over 2 million people worldwide. MS results in disabling and troublesome symptoms due to damage to the brain and spinal cord. Pharmaceutical options exist for the management of MS and its associated symptoms. Some individuals with MS utilize Cannabis to help manage their symptoms. Cannabinoid use in MS animal models have shown promise, and evidence supporting the indication(s) for Cannabis in MS is rapidly evolving and highly relevant to clinical practice. Currently one formulation of Cannabis (Sativex®) has Health Canada approval as an adjunct treatment option for MS-related spasticity and pain. A systematic review was conducted to examine the literature on Cannabis-based medicine (CBM) use in MS. Medline, Embase, and International Pharmaceutical Abstracts were searched for articles related to MS and CBM in February 2018. All human studies, with outcomes specific to MS, and published in English, were eligible for inclusion. There was no publication year limit and no restrictions based on study design. Articles were screened independently by two reviewers, first by title and then by abstract. Two reviewers then independently performed data extraction on all included articles, and a quality assessment using a modified Downs and Black assessment tool. Included articles were categorized by their primary outcome into the following categories: spasticity, tremor, pain, cognition, balance/walking, bladder dysfunction, general symptoms, adverse events/safety, or disease progression. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After removal of duplicates, 2058 articles were identified, with 60 studies meeting the inclusion criteria. Twenty-six articles were randomized controlled studies and 34 utilized a non-randomized study design. Cannabidiol and delta-9-tetrahydrocannabidiol oromucosal spray (Sativex®) was the most commonly studied CBM for MS. The dose size and frequency of administration between studies was inconsistent. Spasticity was the most common MS symptom to be treated with CBM (n=29), followed by pain (n=8) and cognition (n=6). Twenty-three studies were poor quality, 14 were fair quality, and 23 were good/excellent quality. CBM showed a trend of reducing spasticity and pain in individuals with MS; however, the variable quality of the evidence requires consideration when examining results of individual studies. Adverse events were frequent but mild, and CBM was well tolerated. This systematic review outlines the potential of CBM to treat MS spasticity and pain, however more research is needed to examine its use for other MS symptoms. Additionally, the use of other cannabinoid products for MS treatment, the effects of administering CBM with current MS medications, and possible long-term impacts of CBM in those with MS need to be investigated further.

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)0.006
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.011
Bibliometrics0.0070.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.080
GPT teacher head0.256
Teacher spread0.176 · 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 designSystematic review
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

Citations0
Published2019
Admission routes1
Has abstractyes

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