Evidence map›Paper›PMID 37843578›Full record

ReviewInnere Medizin (Heidelberg, Germany)2023

[Digital rheumatology].

Johannes Knitza, Sebastian Kuhn

Abstract readEnglish AbstractReview
PubMed Publisher
In one paragraph

Review in Innere Medizin (Heidelberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Johannes KnitzaInstitut für Digitalisierung in der Medizin, Universitätsklinikum Gießen und Marburg, Philipps-Universität Marburg, Baldingerstr., 35043, Marburg, Deutschland. knitza@uni-marburg.de.
Sebastian KuhnInstitut für Digitalisierung in der Medizin, Universitätsklinikum Gießen und Marburg, Philipps-Universität Marburg, Baldingerstr., 35043, Marburg, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic inflammatory rheumatic diseases mostly run an undulating course and with unspecific symptoms. The initial clarification and timely initiation of treatment are challenging, which is additionally exacerbated by the lack of specialized physicians. Digital approaches, including artificial intelligence (AI), should be of assistance and enable an improved, personalized and needs-based treatment; however, the evidence is currently still very limited. This article provides a compact overview of the current state of digital rheumatology.

Indexed as

RheumatologyArtificial IntelligenceHumansPalliative CareArtificial intelligenceChronic inflammatory diseasesPersonalized medicineSymptom checkersTelemedicine

Identifiers

PMID37843578

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.