SynthesisPloS one2020
Systematic review of prediction models in relapsing remitting multiple sclerosis.
Synthesis in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- Prognostic models for amyotrophic lateral sclerosis: a systematic review.Journal of neurology · 2021Pooled it
- Prognostic factors in paediatric-onset multiple sclerosis: a narrative review.Journal of neurology · 2026Review
- Knockoff-Based Fine Mapping of MS-Associated SNPs in Sardinian Trios.Biochemical genetics · 2026Article
- Machine learning approaches for risk prediction in aortic dissection: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Review
- Study on the relationship between disease progression and changes in Th17/Treg levels in patients with relapsing-remitting multiple sclerosis.Frontiers in neurology · 2026Article
- Refining Prognostic Factors in Adult-Onset Multiple Sclerosis: A Narrative Review of Current Insights.International journal of molecular sciences · 2025Review
- Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data.NPJ digital medicine · 2025Article
- Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study.PLOS digital health · 2024Article
- Framework for personalized prediction of treatment response in relapsing-remitting multiple sclerosis: a replication study in independent data.BMC medical research methodology · 2024Article
- Prognostic relevance of MRI in early relapsing multiple sclerosis: ready to guide treatment decision making?Therapeutic advances in neurological disorders · 2024Review
- Beyond lines of treatment: embracing early high-efficacy disease-modifying treatments for multiple sclerosis management.Therapeutic advances in neurological disorders · 2024Review
- Understanding who benefits most from cognitive rehabilitation for multiple sclerosis: A secondary data analysis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2023Article
- Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.The Cochrane database of systematic reviews · 2023Review
- TSPO-Detectable Chronic Active Lesions Predict Disease Progression in Multiple Sclerosis.Neurology(R) neuroimmunology & neuroinflammation · 2023Article
- Retrospective cohort study to devise a treatment decision score predicting adverse 24-month radiological activity in early multiple sclerosis.Therapeutic advances in neurological disorders · 2023Article
- FutureMS cohort profile: a Scottish multicentre inception cohort study of relapsing-remitting multiple sclerosis.BMJ open · 2022Observational
- Motor evoked potentials for multiple sclerosis, a multiyear follow-up dataset.Scientific data · 2022Article
- Predictive MRI Biomarkers in MS-A Critical Review.Medicina (Kaunas, Lithuania) · 2022Review
- Using personalized prognosis in the treatment of relapsing multiple sclerosis: A practical guide.Frontiers in immunology · 2022Review
- Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis.Journal of personalized medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
The natural history of relapsing remitting multiple sclerosis (RRMS) is variable and prediction of individual prognosis challenging. The inability to reliably predict prognosis at diagnosis has important implications for informed decision making especially in relation to disease modifying therapies. We conducted a systematic review in order to collate, describe and assess the methodological quality of published prediction models in RRMS. We searched Medline, Embase and Web of Science. Two reviewers independently screened abstracts and full text for eligibility and assessed risk of bias. Studies reporting development or validation of prediction models for RRMS in adults were included. Data collection was guided by the checklist for critical appraisal and data extraction for systematic reviews (CHARMS) and applicability and methodological quality assessment by the prediction model risk of bias assessment tool (PROBAST). 30 studies were included in the review. Applicability was assessed as high risk of concern in 27 studies. Risk of bias was assessed as high for all studies. The single most frequently included predictor was baseline EDSS (n = 11). T2 Lesion volume or number and brain atrophy were each retained in seven studies. Five studies included external validation and none included impact analysis. Although a number of prediction models for RRMS have been reported, most are at high risk of bias and lack external validation and impact analysis, restricting their application to routine clinical practice.
Indexed as
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What OpenQuestion holds
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.