Evidence map›Paper›PMID 37863637›Full record

ArticleKidney international2023

Predicting pediatric kidney disease progression-are 3 variables all you need?

Clarkson R Crane, Pranav S Garimella, Georg Heinze

Abstract readComment
PubMed Publisher
In one paragraph

Article in Kidney international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 44% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 2 countries.

Clarkson R CraneDivision of Nephrology-Hypertension, Department of Medicine, University of California San Diego, San Diego, California, USA; Division of Pediatric Nephrology, Department of Pediatrics, University of California San Diego, San Diego, California, USA.
Pranav S GarimellaDivision of Nephrology-Hypertension, Department of Medicine, University of California San Diego, San Diego, California, USA.
Georg HeinzeCenter for Medical Data Science, Institute of Clinical Biometrics, Medical University of Vienna, Vienna, Austria. Electronic address: georg.heinze@meduniwien.ac.at.
Medical University of Vienna · ATUniversity of California San Diego · USUniversity of San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate estimation of chronic kidney disease (CKD) progression risk is vital for clinical decision-making. Existing risk equations lack validation in pediatric CKD populations. Ng et al. developed new risk equations using the CKD in Children and European Study Consortium for Chronic Kidney Disorders Affecting Pediatric Patients cohorts. The elementary model, incorporating estimated glomerular filtration rate, urine protein-creatinine ratio, and diagnosis, exhibited excellent discrimination and calibration at external validation. External validation of enriched models is pending. The equations have the potential to aid pediatric CKD centers in patient counseling and care planning.

Indexed as

Renal Insufficiency, ChronicChildCreatinineDisease ProgressionGlomerular Filtration RateHumansKidneyKidney Function TestsCreatinine

Identifiers

PMID37863637
OpenAlexW4387760441

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.