Evidence map›Paper›PMID 39651898›Full record

ReviewEuropean journal of rheumatology2024

The COVID-19 Pandemic Heightens Interest in Cytokine Storm Disease and Advances in Machine Learning Diagnosis, Telemedicine, and Primordial Prevention of Rheumatic Diseases.

Tomohiro Koga, Shin-Ya Kawashiri, Fumiaki Nonaka, Yoshika Tsuji, Mami Tamai, Atsushi Kawakami

Abstract readReview
In one paragraph

Review in European journal of rheumatology, 2024. 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. Review
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

6 authors.

Tomohiro KogaDivision of Advanced Preventive Medical Sciences, Department of Immunology and Rheumatology, Nagasaki University Graduate School of Biomedical Sciences, Sakamoto, Nagasaki, Japan.
Shin-Ya KawashiriDivision of Advanced Preventive Medical Sciences, Department of Immunology and Rheumatology, Nagasaki University Graduate School of Biomedical Sciences, Sakamoto, Nagasaki, Japan.
Fumiaki NonakaDepartment of Community Medicine, Nagasaki University Graduate School of Biomedical Sciences, Sakamoto, Nagasaki, Japan.
Yoshika TsujiDivision of Advanced Preventive Medical Sciences, Department of Immunology and Rheumatology, Nagasaki University Graduate School of Biomedical Sciences, Sakamoto, Nagasaki, Japan.
Mami TamaiDivision of Advanced Preventive Medical Sciences, Department of Immunology and Rheumatology, Nagasaki University Graduate School of Biomedical Sciences, Sakamoto, Nagasaki, Japan.
Atsushi KawakamiDivision of Advanced Preventive Medical Sciences, Department of Immunology and Rheumatology, Nagasaki University Graduate School of Biomedical Sciences, Sakamoto, Nagasaki, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Insights gained during the coronavirus disease 2019 pandemic has underscored the critical role played by both innate and adaptive immune responses in determining the severity of diseases. This newfound understanding holds significant potential to bring about a paradigm shift in the diagnosis, treatment, and management of autoimmune conditions. Advanced technologies that are emerging in the field are expected to play a pivotal role in this transformation. These include the utilization of multi-omics analysis to stratify disease states, the application of precision medicine through the integration of digital technologies, and the implementation of telemedicine to bridge existing regional disparities in healthcare provision. The objective of this descriptive review is to offer a detailed overview of reclassifying cytokine storm diseases, explore the use of machine learning methodologies in autoimmune diseases, and highlight the importance of incorporating telemedicine and innovative prevention strategies into the management of rheumatoid arthritis. Through this review, we aim to present the most recent research findings and expert insights, and discuss the future prospects and directions in these areas of research.

Indexed as

Cytokine storm diseasesmachine learningprimordial preventiontelemedicine

Identifiers

PMID39651898
PMCPMC11639611

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.