Evidence map›Paper›PMID 23606120›Full record

ReviewImmunologic research2013

Detecting multiple autoantibodies to diagnose autoimmune co-morbidity (multiple autoimmune syndromes and overlap syndromes): a challenge for the autoimmunologist.

Renato Tozzoli, Maria Concetta Sorrentino, Nicola Bizzaro

Abstract readReview
PubMed Publisher
In one paragraph

Review in Immunologic research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.8field-weighted citation impact, top 11% of its field
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

11 citing papers in PubMed, 33 citations in OpenAlex.

  1. Observational
  2. Review
  3. Article
  4. Article
  5. The prediction effects of thyroid function in the severity of Guillain-Barré syndrome.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2022
    Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. 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

3 authors at 3 institutions in 2 countries.

Renato TozzoliDepartment of Laboratory Medicine, S. Maria degli Angeli Hospital, Pordenone, Italy.
Maria Concetta Sorrentino
Nicola Bizzaro
Azienda Ospedaliera Santa Maria Degli Angeli · ITIstituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione · ITOspedale Sant Antonio · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the last 15 years, the estimated prevalence of autoimmune diseases increased from about 4 to 9.4%. Autoimmune co-morbidity (i.e., the association between two or more autoimmune diseases in the same patient) is present in 0.4-0.5% of the worldwide population, more frequently in patients with multiple sclerosis, rheumatoid arthritis, autoimmune thyroid disease, type 1 diabetes, inflammatory bowel disease and vitiligo. The most common associations between autoimmune diseases define two clusters: the multiple autoimmune syndromes and overlap syndromes. This review highlights the importance of multiple antibody assays in the diagnosis of multiple autoimmune and overlap syndromes and summarizes the general conditions of autoantibody detection using the model of the five W's: why, who, what, when and where. These conditions represent a new challenge for clinical and laboratory autoimmunologists, who, in a multidisciplinary arena, cooperate in investigating and treating multiple autoimmune disorders.

Indexed as

AnimalsAutoantibodiesAutoimmune DiseasesCluster AnalysisComorbidityHumansInterdisciplinary CommunicationItalyPractice Guidelines as TopicSerologic TestsAutoantibodies

Identifiers

PMID23606120
OpenAlexW1987801744

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.