Evidence map›Paper›PMID 26000143›Full record

ReviewAuto- immunity highlights2013

Detection of autoantibodies in a point-of-care rheumatology setting.

Konstantin N Konstantinov, Antonios Tzamaloukas, Robert L Rubin

Abstract readReview
In one paragraph

Review in Auto- immunity highlights, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

3 authors.

Konstantin N KonstantinovDivision of Rheumatology, Department of Internal Medicine, University of New Mexico Health Sciences Center, Mail Stop MSC10-5550, Albuquerque, NM 87131 USA.
Antonios TzamaloukasRenal Section, Raymond G. Murphy VA Medical Center, 1501 San Pedro SE, Albuquerque, NM 87108 USA.
Robert L RubinDepartment of Molecular Genetics and Microbiology, MCS08-4660, University of New Mexico Health Sciences Center, Albuquerque, NM 87131 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autoimmune rheumatic diseases are common and confront society with serious medical, social, and financial burdens imposed by their debilitating nature. Many autoimmune diseases are associated with a particular set of autoantibodies, which have emerged as highly useful to define and classify disease, predict flares, or monitor efficacy of therapy. However, current practice for monitoring autoantibodies is protracted, labor-intensive, and expensive. This review provides an overview on the value of point-of-care (POC) biosensor technology in the diagnosis and management of patients with autoimmune rheumatic diseases. Real-time measurement of autoantibodies will clearly benefit the rheumatology practice in emergency and urgent care settings, where definitive diagnosis is essential for initiation of correct critical care therapy. Immediate serological information in clinic will provide considerable value for long-term patient care and an opportunity for an instant, result-deduced therapeutic action, avoiding delays and improving compliance, especially in field-based and remote areas. We describe the particular autoantibodies that are useful disease and activity markers and would, therefore, be attractive to POC applications. Already existing biosensors and platforms that show promise for autoantibody testing are summarized and comparatively evaluated. As POC assessment is gaining momentum in several areas of patient care, we propose that rheumatology is poised to benefit from this innovative and affordable technology.

Indexed as

AutoantibodiesAutoimmunityPoint-of-care (POC) testingRheumatic diseases

Identifiers

PMID26000143
PMCPMC4389050

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.