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Record W4414794331 · doi:10.1002/ana.78047

From Diagnosis to Disease Staging: Multisite Validation of Cerebrospinal Fluid Molecular Tests in Multiple Sclerosis

2025· article· en· W4414794331 on OpenAlexaff
Laura Ghezzi, Péter Kósa, Mark Greenwood, Enrique Álvarez, Christine Freedman, Anne H. Cross, Francesca Pace, Mark S. Freedman, Joanna Kocot, Laura Piccio, Bibiana Bielekova

Bibliographic record

VenueAnnals of Neurology · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsMultiple sclerosisDiseaseCerebrospinal fluidMulticenter studyDegenerative diseaseCentral nervous system diseaseClinical neurology

Abstract

fetched live from OpenAlex

Objective The growing demand for personalized treatment in multiple sclerosis (MS) highlights the need for more precise biomarkers that can outperform magnetic resonance imaging and clinical assessment in patient stratification. Advances in multiplex proteomic technologies suggest that cerebrospinal fluid (CSF) analysis at MS onset may not only improve diagnostic accuracy, but also offer prognostic and staging information, as well as insight into molecular therapeutic targets. Methods This multicenter study retrospectively analyzed cryopreserved CSF samples from 160 individuals undergoing diagnostic evaluation for possible neuroimmunological disorder, and among these, followed a cohort of 96 people with confirmed MS for at least 3 years. The goal was to externally validate previously published CSF‐based diagnostic and prognostic classifiers. Results Upon unblinding, the CSF‐based molecular diagnostic test distinguished 96 people with confirmed MS from 30 individuals with other inflammatory neurological diseases, and 34 individuals with non‐inflammatory neurological diseases, achieving an area under the receiver operating characteristic curve of 0.94 ( p = 4.7 × 10 −21 ). The test also differentiated 65 individuals with relapsing–remitting MS from 31 individuals with progressive MS, with an area under the receiver operating characteristic curve of 0.76 ( p = 1.4 × 10 −5 ). The prognostic classifier predicted prospectively measured Expanded Disability Status Scale scores at follow up (rho = 0.43, p = 2.54 × 10 −5 ). Interpretation This multicenter external validation study demonstrates that CSF‐based molecular tests can robustly distinguish MS from other neurological conditions, stratify MS subtypes, and predict future disability progression in real‐world settings. These results lay the groundwork for development of next‐generation molecular tools to personalize care in MS. ANN NEUROL 2026;99:328–340

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)0.023
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.058
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.127
GPT teacher head0.376
Teacher spread0.250 · 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 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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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