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Diagnosis of multiple sclerosis: 2024 revisions of the McDonald criteria
Xavier Montalbán, Christine Lebrun‐Frénay, Jiwon Oh, Georgina Arrambide, Marcello Moccia, Maria Pia Amato, Lilyana Amezcua, Brenda Banwell, Amit Bar-Or, Frederik Barkhof, Helmut Butzkueven, Olga Ciccarelli, Jeremy Chataway, Jeffrey A. Cohen, Giancarlo Comi, Jorge Correale, Florian Deisenhammer, Massimo Filippi, Julie Fiol, Mark S. Freedman, Kazuo Fujihara, Cristina Granziera, Ari Green, Hans-Peter Hartung, Kerstin Hellwig, L Kappos, Dorlan Kimbrough, Joep Killestein, Fred Lublin, Romain Marignier, Ruth Ann Marrie, Aaron Miller, Susana Otero-Romero, Daniel Ontaneda, Sudarshini Ramanathan, Daniel Reich, Maria A. Rocca, Àlex Rovira, Shiv Saidha, Amber Salter, Jaume Sastre‐Garriga, Deanna Saylor, Andrew Solomon, Maria Pia Sormani, Bruno Stankoff, Mar Tintoré, Helen Tremlett, Anneke van der Walt, Shanthi Viswanathan, Heinz Wiendl, Brigitte Wildemann, Bassem Yamout, Paola Zaratin, Peter A. Calabresi, Timothy Coetzee, Alan J. Thompson
Bibliographic record
VenueThe Lancet Neurology · 2025
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of British ColumbiaNova Scotia Health AuthorityOttawa HospitalUniversity of TorontoDalhousie UniversityUniversity of OttawaSt. Michael's Hospital
FundersNational Institute of Neurological Disorders and StrokeChugai PharmaceuticalNovartis PharmaInstituto de Salud Carlos IIIPfizer FoundationEMD SeronoNational Institutes of HealthMedDay PharmaceuticalsNational Health and Medical Research CouncilNeuraxpharmIpsenUniversity College London Hospitals NHS Foundation TrustShionogiJanssen PharmaceuticalsSanofi GenzymeNovo NordiskSamsungEisaiUniversity of TorontoMinistero della SaluteIXICOBMA Foundation for Medical ResearchDalhousie UniversityFondazione CariploZonMwUniversity of SydneySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMinistry of Education, Culture, Sports, Science and TechnologyMultiple Sclerosis International FederationPublic Health AgencyMultiple Sclerosis AustraliaIdorsia PharmaceuticalsH. Lundbeck A/SRosetrees TrustFondation pour la Recherche MédicaleBayer HealthCareUniversity College LondonSchweizerische Multiple Sklerose GesellschaftMultiple Sclerosis SocietyFondation pour l'Aide à la Recherche sur la Sclérose en PlaquesProthenaArgenxFondation CharcotCelltrionCVS HealthMylanCanadian Institutes of Health ResearchMultiple Sclerosis Society of CanadaRace to Erase MSDeutsche ForschungsgemeinschaftArthritis SocietyMedical Research CouncilPatient-Centered Outcomes Research InstituteHope FoundationKiniksa PharmaceuticalsAlexion PharmaceuticalsMyelin Repair FoundationAmgenPublic Health Agency of CanadaGenentechAstraZenecaEuropean CommissionAmerican Academy of NeurologyFondation Brain CanadaEuropean Committee for Treatment and Research in Multiple SclerosisUniversity of PennsylvaniaBristol-Myers SquibbTeva Pharmaceutical IndustriesCrohn's and Colitis CanadaBioCrystFondazione Italiana Sclerosi MultiplaCelgeneModernaPfizerBiogenAllerganF. Hoffmann-La RocheSanofiTG TherapeuticsEli Lilly and CompanyRegione ToscanaPetre FoundationU.S. Department of DefenseHorizon TherapeuticsAmicus TherapeuticsMultiple Sclerosis Scientific Research FoundationNational Multiple Sclerosis SocietyU.S. Department of Health and Human ServicesGlaxoSmithKlineNational Research FoundationNational Institute for Health and Care ResearchCSL BehringRoyal Australasian College of PhysiciansNational Science Foundation
KeywordsMultiple sclerosisMcDonald criteriaClinically isolated syndromeClinical PracticeDisease
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.016
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.086
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 head0.221
GPT teacher head0.412
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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
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
Citations299
Published2025
Admission routes2
Has abstractno