ReviewBMC medicine2012
Personalized medicine in multiple sclerosis: hope or reality?
Review in BMC medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Patient profiling in relapsing multiple sclerosis: insights from a real-world observational study into the unmet medical needs of patients on disease-modifying therapy.Journal of neurology · 2026Observational
- Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.The Cochrane database of systematic reviews · 2023Review
- Multiple sclerosis and computational biology (Review).Biomedical reports · 2022Review
- The Brave New World of Early Treatment of Multiple Sclerosis: Using the Molecular Biomarkers CXCL13 and Neurofilament Light to Optimize Immunotherapy.Biomedicines · 2022Review
- Personalizing Medicine and Technologies to Address the Experiences and Needs of People with Multiple Sclerosis.Journal of personalized medicine · 2021Article
- CSF proteome in multiple sclerosis subtypes related to brain lesion transcriptomes.Scientific reports · 2021Article
- Engineered immunological niches to monitor disease activity and treatment efficacy in relapsing multiple sclerosis.Nature communications · 2020Article
- Recent Advances in Antigen-Specific Immunotherapies for the Treatment of Multiple Sclerosis.Brain sciences · 2020Review
- Dynamic Learning of Patient Response Types: An Application to Treating Chronic Diseases.Management science · 2018Article
- Beyond the Magic Bullet: Current Progress of Therapeutic Vaccination in Multiple Sclerosis.CNS drugs · 2018Article
- Treatment-Related Progressive Multifocal Leukoencephalopathy in Multiple Sclerosis: A Comprehensive Review of Current Evidence and Future Needs.Drug safety · 2016Review
- Three immune-mediated disease models induced by Theiler's virus: Multiple sclerosis, seizures and myocarditis.Clinical & experimental neuroimmunology · 2016Article
- Lateral and escalation therapy in relapsing-remitting multiple sclerosis: a comparative study.Journal of neurology · 2016Article
- Multiple sclerosis: clinical profiling and data collection as prerequisite for personalized medicine approach.BMC neurology · 2016Review
- PET imaging in multiple sclerosis.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2014Review
- Personalized medicine in neurodegenerative diseases: how far away?Molecular diagnosis & therapy · 2014Review
- The emerging agenda of stratified medicine in neurology.Nature reviews. Neurology · 2014Review
- Diffusion Tensor Imaging in NAWM and NADGM in MS and CIS: Association with Candidate Biomarkers in Sera.Multiple sclerosis international · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Personalized treatment is highly desirable in multiple sclerosis because it is an immensely heterogeneous disease. This heterogeneity is seen in both the disease course and the treatment responses. Currently, a combination of clinical features and imaging parameters in magnetic resonance imaging is used to classify active and non-active patients and treatment responders and non-responders. Although this classification works on a group level, individual patients often behave differently from the group. Therefore additional biomarkers are needed to provide better indicators for prognosis and treatment response. Basic and clinical research have discovered different promising targets. It is now essential to verify the utility and accuracy of these markers in large, prospectively sampled patient cohorts.
Indexed as
Identifiers
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.