Evidence map›Paper›PMID 38767543›Full record

ReviewPathologica2024

Systemic vasculitis involving the kidney: the nephropathologist's point of view.

Vincenzo L'Imperio, Stefano Ceola, Bruna Cerbelli, Antonella Barreca, Fabio Pagni

Abstract readReview
In one paragraph

Review in Pathologica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

5 authors.

Vincenzo L'ImperioDepartment of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy.
Stefano CeolaDepartment of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy.
Bruna CerbelliDepartment of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, Rome, Italy.
Antonella Barreca *Pathology Unit, Città della Salute e della Scienza di Torino University Hospital, Turin, Italy.
Fabio Pagni *Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kidneys are often targets of systemic vasculitis (SVs), being affected in many different forms and representing a possible sentinel of an underlying multi-organ condition. Renal biopsy still remains the gold standard for the identification, characterization and classification of these diseases, solving complex differential diagnosis thanks to the combined application of light microscopy (LM), immunofluorescence (IF) and electron microscopy (EM). Due to the progressively increasing complexity of renal vasculitis classification systems (e.g. pauci-immune vs immune complex related forms), a clinico-pathological approach is mandatory and adequate technical and interpretative expertise in nephropathology is required to ensure the best standard of care for our patients. In this complex background, the present review aims at summarising the current knowledge and challenges in the world of renal vasculitis, unveiling the potential role of the introduction of digital pathology in this setting, from the creation of hub-spoke networks to the future application of artificial intelligence (AI) tools to aid in the diagnostic and scoring/classification process.

Indexed as

KidneyArtificial IntelligenceBiopsyDiagnosis, DifferentialHumansKidney DiseasesSystemic VasculitisANCAcryoglobulinemiaIgA vasculitisrenal biopsysystemic vasculitis

Identifiers

PMID38767543
PMCPMC11138762

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.