ReviewFrontiers in neurology2021
Predicting Multiple Sclerosis: Challenges and Opportunities.
Review in Frontiers in neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Prevalence of epsilon toxin-producing Clostridium perfringens isolates among patients with multiple sclerosis and neuromyelitis optica spectrum disorder in Iran.BMC neurology · 2025Article
- The Polygenic Nature of Multiple Sclerosis: Genetic Variants, Immunological Modulation, and Environmental Connections.Endocrine, metabolic & immune disorders drug targets · 2025Review
- Multiple sclerosis risk variants influence the peripheral B-cell compartment early in life in the general population.European journal of neurology · 2023Article
- Decision trees to evaluate the risk of developing multiple sclerosis.Frontiers in neuroinformatics · 2023Article
- Polygenic risk score prediction of multiple sclerosis in individuals of South Asian ancestry.Brain communications · 2023Article
- Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human-Machine Intelligence.Sensors (Basel, Switzerland) · 2022Article
- Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Determining effective means of preventing Multiple Sclerosis (MS) relies on testing preventive strategies in trial populations. However, because of the low incidence of MS, demonstrating that a preventive measure has benefit requires either very large trial populations or an enriched population with a higher disease incidence. Risk scores which incorporate genetic and environmental data could be used, in principle, to identify high-risk individuals for enrolment in preventive trials. Here we discuss the concepts of developing predictive scores for identifying individuals at high risk of MS. We discuss the empirical efforts to do so using real cohorts, and some of the challenges-both theoretical and practical-limiting this work. We argue that such scores could offer a means of risk stratification for preventive trial design, but are unlikely to ever constitute a clinically-helpful approach to predicting MS for an individual.
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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.