MétaCan
Menu
Back to cohort
Record W4318616058 · doi:10.3390/ctn7010005

Saudi Consensus Recommendations on the Management of Multiple Sclerosis: Diagnosis and Radiology/Imaging

2023· article· en· W4318616058 on OpenAlexaff
Jameelah Saeedi, Rumaiza H. Al-Yafeai, Abdulaziz M. AlAbdulSalam, Abdulaziz Y. Al-Dihan, Azeeza Aldwaihi, Awad A. Al Harbi, Yaser I. Aljadhai, Ahmed Al‐Jedai, Nuha M. Alkhawajah, Majed Alluqmani, Abdulrahman Almalki, Hajer Almudaiheem, Hind Alnajashi, Rayan A. Alshareef, Amani Alshehri, Faisal Y. AlThekair, Nabila S. Ben Slimane, Edward Cupler, Mamdouh H. Kalakatawi, Hanaa Kedah, Yaser Al Malik, I. Althubaiti, Reem F. Bunyan, Eslam Shosha, Mohammed Aljumah

Bibliographic record

VenueClinical and Translational Neuroscience · 2023
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineMultidisciplinary approachHealth carePopulationIncidence (geometry)Multiple sclerosisMEDLINEFamily medicineIntensive care medicineEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) is an inflammatory neurological illness common in young adults. The prevalence and incidence of MS are regionally and globally increasing. Recent data from Saudi Arabia (SA) estimate the prevalence to be 40.40 cases per 100,000 population, and 61.95 cases per 100,000 population for Saudi nationals. With the increasing availability of treatment options, new challenges for treatment selection and approaches have emerged. There is a clear need for national guidelines to standardize practice, guide the personalization of decisions, and contain increasing costs. A multidisciplinary expert panel was formed to develop evidence-based Saudi consensus recommendations on the diagnosis and clinical care of MS, to aid healthcare practitioners in advising patients on treatment decisions. The recommendations were agreed upon after a thorough review, an evaluation of existing international guidelines, and the latest emerging evidence.

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)0.021
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 score0.021
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0060.003
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0050.003
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0060.004

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 head0.311
GPT teacher head0.414
Teacher spread0.102 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueClinical and Translational NeuroscienceSame topicMultiple Sclerosis Research StudiesFrench-language works237,207