Evidence map›Paper›PMID 39232852›Full record

ArticleNeurological research and practice2024

Prognostic models in multiple sclerosis: progress and challenges in clinical integration.

Joachim Havla, Kelly Reeve, Begum Irmak On, Ulrich Mansmann, Ulrike Held

Abstract readLetter
In one paragraph

Article in Neurological research and practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Joachim Havlalnstitute of Clinical Neuroimmunology, LMU University Hospital, LMU Munich, Munich, Germany. joachim.havla@med.lmu.de.ORCID http://orcid.org/0000-0002-4386-1340
Kelly ReeveEpidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-9325-6467
Begum Irmak OnInstitute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany.ORCID https://orcid.org/0000-0002-9008-0141
Ulrich MansmannInstitute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany.ORCID https://orcid.org/0000-0002-9955-8906
Ulrike HeldEpidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-3105-5840

Funding

Bundesministerium für Bildung, Wissenschaft und Kultur Grant Numbers 01ZZ1603[A-D] and 01ZZ1804[A-H] (DIFUTURE)
6 · The paper itself

Abstract

As a chronic inflammatory disease of the central nervous system, multiple sclerosis (MS) is of great individual health and socio-economic significance. To date, there is no prognostic model that is used in routine clinical care to predict the very heterogeneous course of the disease. Despite several research groups working on different prognostic models using traditional statistics, machine learning and/or artificial intelligence approaches, the use of published models in clinical decision making is limited because of poor model performance, lack of transferability and/or lack of validated models. To provide a systematic overview, we conducted a "Cochrane review" that assessed 75 published prediction models using relevant checklists (CHARMS, PROBAST, TRIPOD). We have summarized the relevant points from this analysis here so that the use of prognostic models for therapy decisions in clinical routine can be successful in the future.

Indexed as

Multiple sclerosisPersonalized medicinePrognostic modelTreatment decisionValidation

Identifiers

PMID39232852
PMCPMC11376049

What OpenQuestion holds

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Registered trials

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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.