Evidence map›Paper›PMID 37407744›Full record

ReviewAbdominal radiology (New York)2023

Multimodality pictorial review of IgG4-related disease in the abdomen and pelvis.

Suzanne Czerniak, Aditya Rao, Mahan Mathur

Abstract readReview
PubMed Publisher
In one paragraph

Review in Abdominal radiology (New York), 2023. 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
0.5field-weighted citation impact, top 32% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

3 authors at 2 institutions in 1 country.

Suzanne CzerniakDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, Room TE-2, PO Box 208042, New Haven, CT, 06520, USA. suzanne.czerniak@yale.edu.ORCID http://orcid.org/0000-0002-7893-6680
Aditya RaoDiagnostic Radiology Resident, Yale-New Haven Hospital, New Haven, CT, USA.
Mahan MathurDepartment of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, Room TE-2, PO Box 208042, New Haven, CT, 06520, USA.
Yale University · USYale New Haven Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImmunoglobulin G4-related disease (IgG4-RD) is a systemic, immune-mediated disease that can affect multiple organs, including the orbits, salivary glands, thyroid gland, lungs, aorta, pancreas, bile ducts, lymph nodes, and retroperitoneum. While timely diagnosis is particularly important given the efficacy of glucocorticoid treatment for IgG4-RD, accurate recognition can prove a challenge given the overlap between the imaging features of this disease and other entities. PURPOSE: After a review of the epidemiology, pathophysiology, and clinical considerations (including treatment) associated with IgG4-RD, this pictorial review will showcase the variable imaging manifestations of this disease in the abdomen and pelvis. Post-treatment imaging appearance of these entities will be reviewed and mimickers of this disease in the abdomen and pelvis will be presented.

conclusionThe presence of mass-like soft tissue with radiographic characteristics of fibrosis affecting multiple organs should raise suspicion for IgG4-RD, although definite diagnosis can only be made with appropriate clinical, serological, and pathologic data.

Indexed as

Autoimmune DiseasesImmunoglobulin G4-Related DiseaseAbdomenFibrosisHumansPelvisAutoimmune pancreatitisIgG4IgG4-RDIgG4-related diseaseRetroperitoneal fibrosisSclerosing cholangitis

Identifiers

PMID37407744
OpenAlexW4383302334

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.