ReviewClinical and experimental immunology2014
Management of disease-modifying treatments in neurological autoimmune diseases of the central nervous system.
Review in Clinical and experimental immunology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Development of Registry Data to Create Interactive Doctor-Patient Platforms for Personalized Patient Care, Taking the Example of the DESTINY System.Frontiers in digital health · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The therapeutic armamentarium for autoimmune diseases of the central nervous system, specifically multiple sclerosis and neuromyelitis optica, is steadily increasing, with a large spectrum of immunomodulatory and immunosuppressive agents targeting different mechanisms of the immune system. However, increasingly efficacious treatment options also entail higher potential for severe adverse drug reactions. Especially in cases failing first-line treatment, thorough evaluation of the risk-benefit profile of treatment alternatives is necessary. This argues for the need of algorithms to identify patients more likely to benefit from a specific treatment. Moreover, paradigms to stratify the risk for severe adverse drug reactions need to be established. In addition to clinical/paraclinical measures, biomarkers may aid in individualized risk-benefit assessment. A recent example is the routine testing for anti-John Cunningham virus antibodies in natalizumab-treated multiple sclerosis patients to assess the risk for the development of progressive multi-focal leucoencephalopathy. Refined algorithms for individualized risk assessment may also facilitate early initiation of induction treatment schemes in patient groups with high disease activity rather than classical escalation concepts. In this review, we will discuss approaches for individiualized risk-benefit assessment both for newly introduced agents as well as medications with established side-effect profiles. In addition to clinical parameters, we will also focus on biomarkers that may assist in patient selection.
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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.