Evidence map›Paper›PMID 36442487›Full record

ReviewLancet (London, England)2023

Systemic sclerosis.

Elizabeth R Volkmann, Kristofer Andréasson, Vanessa Smith

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Lancet (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 345 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
345citing papers in PubMed, 7 pooled it
–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

345 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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285 more citing papers are in PubMed but not listed here.

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.

Elizabeth R VolkmannDepartment of Medicine, Division of Rheumatology, University of California, Los Angeles, CA, USA; David Geffen School of Medicine, Los Angeles, CA, USA. Electronic address: evolkmann@mednet.ucla.edu.
Kristofer AndréassonDepartment of Rheumatology, Lund University, Lund, Sweden.
Vanessa SmithDepartment of Internal Medicine and Department of Rheumatology, Ghent University (Hospital), Ghent, Belgium; Unit for Molecular Immunology and Inflammation, VIB Inflammation Research Centre, Ghent, Belgium.

Funding

Identifying predictors of treatment response in systemic sclerosis-related interstitial lung diseaseK23HL150237 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI VOLKMANN, ELIZABETH R · 2020 to 2024
$910k
NHLBI NIH HHS K23 HL150237
6 · The paper itself

Abstract

Systemic sclerosis, also known as scleroderma, is a rare and complex autoimmune connective-tissue disease. Once considered an untreatable and unpredictable condition, research advancements have improved our understanding of its disease pathogenesis and clinical phenotypes and expanded our treatment armamentarium. Early and accurate diagnosis is essential, while ongoing efforts to risk stratify patients have a central role in predicting both organ involvement and disease progression. A holistic approach is required when choosing the optimal therapeutic strategy, balancing the side-effect profile with efficacy and tailoring the treatment according to the goals of care of the patient. This Seminar reviews the multiple clinical dimensions of systemic sclerosis, beginning at a precursor very early stage of disease, with a focus on timely early detection of organ involvement. This Seminar also summarises management considerations according to the pathological hallmarks of systemic sclerosis (eg, inflammation, fibrosis, and vasculopathy) and highlights unmet needs and opportunities for future research and discovery.

Indexed as

Autoimmune DiseasesScleroderma, SystemicVascular DiseasesDisease ProgressionFibrosisHumans

Identifiers

PMID36442487
PMCPMC9892343

What OpenQuestion holds

Textmetadata
LicenceTDM
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.