ArticleNature medicine2025
AI-driven reclassification of multiple sclerosis progression.
Article in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Relapse prediction and individualized treatment-effect modeling in relapsing multiple sclerosis: a systematic review.Frontiers in public health · 2026Pooled it
- Towards a biologically informed description of multiple sclerosis disease course - a roadmap for transition.Nature reviews. Neurology · 2026Review
- Clinical trials in non-ambulatory people with progressive multiple sclerosis: can the nine hole peg test step up as primary outcome measure?Journal of neurology · 2026Article
- Interleukin-1β-Induced Inflammatory Signaling and Myelin-Related Alterations in Oligodendroglia and Schwann Cells in Multiple Sclerosis: Modulatory Effects of Ibuprofen and Flurbiprofen.Current issues in molecular biology · 2026Article
- Challenges and future directions for multiple sclerosis after the 2024 McDonald diagnostic criteria.Nature medicine · 2026Article
- Artificial Intelligence That Changes Clinical Neurology Practice: Translating Algorithms Into Actionable Care.Journal of clinical neurology (Seoul, Korea) · 2026Review
- Upper Cervical Cord Area as a Biomarker of Conversion to Secondary Progressive Multiple Sclerosis.Annals of clinical and translational neurology · 2026Article
- Glial cells in chronic inflammation: diversity, dysfunction and therapeutic targeting.Nature reviews. Immunology · 2026Review
- Big data in multiple sclerosis.Current opinion in neurology · 2026Review
- Mechanistic Insights into the Role of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Multiple Sclerosis.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026Review
- Psychotropic medication use and bone fracture risk among patients with multiple sclerosis: a real-world study.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Crop-OCT: a Fully Integrated Imageomics Pipeline to Identify Regional and Focal Retinopathy in Murine Models.bioRxiv : the preprint server for biology · 2026Article
- Multiscale network perspectives on glioma: from tumour biology to symptoms, survival and treatment.Nature reviews. Neurology · 2026Review
- Construction of patient trajectories to model clinical trial outcomes: application to myasthenia gravis.Frontiers in digital health · 2026Article
- Multiple sclerosis as an asynchronous neuroinflammatory system.Frontiers in immunology · 2026Review
- Multiple sclerosis: 2026 update.Free neuropathology · 2026Review
- Ocrelizumab Discontinuation vs. Continuation After Safety Events: Comparative Insights from MSBase.Current neuropharmacology · 2026Article
- Autologous haematopoietic stem cell transplantation stabilizes retinal atrophy in multiple sclerosis.Brain communications · 2026Article
- Article
- Brain Myelin Covariance Networks: Gradients, Cognition, and Higher-Order Landscape.Behavioral sciences (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple sclerosis (MS) affects 2.9 million people. Traditional classification of MS into distinct subtypes poorly reflects its pathobiology and has limited value for prognosticating disease evolution and treatment response, thereby hampering drug discovery. Here we report a data-driven classification of MS disease evolution by analyzing a large clinical trial database (approximately 8,000 patients, 118,000 patient visits and more than 35,000 magnetic resonance imaging scans) using probabilistic machine learning. Four dimensions define MS disease states: physical disability, brain damage, relapse and subclinical disease activity. Early/mild/evolving (EME) MS and advanced MS represent two poles of a disease severity spectrum. Patients with EME MS show limited clinical impairment and minor brain damage. Transitions to advanced MS occur via brain damage accumulation through inflammatory states, with or without accompanying symptoms. Advanced MS is characterized by moderate to high disability levels, radiological disease burden and risk of disease progression independent of relapses, with little probability of returning to earlier MS states. We validated these results in an independent clinical trial database and a real-world cohort, totaling more than 4,000 patients with MS. Our findings support viewing MS as a disease continuum. We propose a streamlined disease classification to offer a unifying understanding of the disease, improve patient management and enhance drug discovery efficiency and precision.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.