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Record W4311791639 · doi:10.3390/ctn6040027

Saudi Consensus Recommendations on the Management of Multiple Sclerosis: Disease-Modifying Therapies and Management of Relapses

2022· article· en· W4311791639 on OpenAlexaff
Yaser Al Malik, Ibtisam A. Al Thubaiti, Maha Alammari, Norah Al Fugham, Eman N. Ali, Dema A. Alissa, Salman Aljarallah, Ahmed Al‐Jedai, Maeed A. AlKathiri, Mona Alkhawajah, Mousa A. Almejally, Hajer Almudaiheem, Hessa S. Al Otaibi, Ghadah H. AlTowaijri, Rumaiza H. Al Yafeai, Mohammed A. Babakkor, Saeed Bohlega, Reem F. Bunyan, Edward Cupler, Mohammed Hakami, Abid Kareem, Amr M. Khardaly, Seraj Makkawi, Leena Saeed, Jameelah Saeedi, Eslam Shosha, Mohammad A. Al Jumah

Bibliographic record

VenueClinical and Translational Neuroscience · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineMultiple sclerosisClinically isolated syndromeIntensive care medicineDisease managementDiseaseMEDLINEInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

For the past 10 years, disease-modifying therapy (DMT) options for multiple sclerosis (MS) have grown remarkably where DMTs have been shown to reduce the risk of MS relapses. MS patients are advised to begin treatment with a DMT shortly after diagnosis to limit the possibility of disease progression over time. While patients with radiologically isolated syndrome do not require pharmacologic treatment, high-risk patients with clinically isolated syndrome are advised to start DMTs. This article provides evidence-based recommendations for DMT use in MS management, helping healthcare practitioners advise patients on treatment decisions. We aim to provide recommendations for the management of acute MS relapses. The recommendations herein were developed following the gathering of a panel of experts after evaluating international guidelines, and the latest evidence was collected through a comprehensive literature review.

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)0.022
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.056
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0070.005
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0060.003
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0070.005

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 head0.276
GPT teacher head0.395
Teacher spread0.119 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2022
Admission routes1
Has abstractyes

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