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Record W7117359334 · doi:10.1177/13524585251398381

Toward a global research agenda for preventing multiple sclerosis

2025· article· en· W7117359334 on OpenAlexafffund
Ruth Ann Marrie, Ruth Dobson, Sergio E. Baranzini, Marco Salvetti, Caroline Bailey, Bruce F. Bebo, Benjamin G. Davis, Rohan Greenland, Jen Lyden, Fiona Mckay, Julia M. Morahan, Julie Pétrin, Pamela Valentine, Bruce Taylor, for the participants in the “Global MS Prevention Workshop”

Bibliographic record

VenueMultiple Sclerosis Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMultiple Sclerosis Society of CanadaNova Scotia Health Authority
FundersMultiple Sclerosis AustraliaMultiple Sclerosis Society of Canada
KeywordsMultiple sclerosisDeveloping countryMEDLINEClinical neurologyGlobal health

Abstract

fetched live from OpenAlex

BACKGROUND: Considerable progress has been made in understanding genetic and environmental risk factors for multiple sclerosis (MS), yet we still cannot prevent MS. OBJECTIVES: To drive progress in primary and secondary prevention of MS by developing a comprehensive research agenda based on current knowledge. METHODS: A global workshop convened people with lived experience, clinicians, researchers, and policymakers with expertise in MS, other chronic diseases, epidemiology, clinical trials, genomics, immunology, virology, behavioral science, and public health to develop pathways to advance the MS prevention agenda. RESULTS: Regarding primary prevention, workshop participants recommended acting on known modifiable risk factors; identifying novel etiological factors and determining how they lead to disease; and building coalitions including organizations with shared interests. Regarding secondary prevention, workshop participants recommended identifying biomarkers relevant from initial immune dysregulation through to initial clinical presentation, linking biomarkers to long-term MS outcomes, developing cost-effective screening tools for MS and point-of-care testing, and developing prodromal criteria for MS that could be implemented clinically. Communication and evaluation frameworks, adoption of an implementation mind-set, and engagement of public health were highlighted as key supporting elements. CONCLUSION: This workshop sets the stage for developing a global prevention agenda for MS.

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)0.006
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.401
GPT teacher head0.424
Teacher spread0.023 · 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.

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

Citations2
Published2025
Admission routes2
Has abstractyes

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