From Diagnosis to Disease Staging: Multisite Validation of Cerebrospinal Fluid Molecular Tests in Multiple Sclerosis
Bibliographic record
Abstract
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
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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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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.
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".