Evidence map›Paper›PMID 31772086›Full record

ArticleBMJ open2019

Proposed minimum information guideline for kidney disease-research and clinical data reporting: a cross-sectional study.

Judit Kumuthini, Christiaan van Woerden, Andrew Mallett, Lyndon Zass, Melek Chaouch, Michael Thompson, Katherine Johnston, Mamana Mbiyavanga, Shakuntala Baichoo, Zahra Mungloo-Dilmohamud and 2 more

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

Judit KumuthiniH3ABioNet, Centre for Proteomic & Genomic Research, Cape Town, South Africa jkumuthini@gmail.com.
Christiaan van WoerdenDepartment of Surgery, Division of Child Urology, University of Cape Town, Cape Town, South Africa.
Andrew MallettKidGen Collaborative and AGHA Renal Genetics Flagships, Parkville, Victoria, Australia.
Lyndon ZassH3ABioNet, Centre for Proteomic & Genomic Research, Cape Town, South Africa.
Melek ChaouchDepartment of Parasitology, Institut Pasteur de Tunis, Tunis, Tunisia.
Michael ThompsonNational Institute of Mathematical Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Katherine JohnstonDepartment of Surgery, Division of Child Urology, University of Cape Town, Cape Town, South Africa.
Mamana MbiyavangaDepartment of Surgery, Division of Child Urology, University of Cape Town, Cape Town, South Africa.
Shakuntala BaichooDepartment of Digital Technologies, University of Mauritius, Reduit, Mauritius.
Zahra Mungloo-DilmohamudDepartment of Digital Technologies, University of Mauritius, Reduit, Mauritius.
Chirag PatelKidGen Collaborative and AGHA Renal Genetics Flagships, Parkville, Victoria, Australia.
Nicola MulderDepartment of Integrative Biomedical Sciences, Division of Computational Biology, University of Cape Town, Cape Town, South Africa.

Funding

H3ABioNet: a sustainable African Bioinformatics Network for H3AfricaU41HG006941 · NHGRI · UNIVERSITY OF CAPE TOWN · PI MULDER, NICOLA · 2012 to 2017
$14.8M
NHGRI NIH HHS U41 HG006941
6 · The paper itself

Abstract

objectiveThis project aimed to develop and propose a standardised reporting guideline for kidney disease research and clinical data reporting, in order to improve kidney disease data quality and integrity, and combat challenges associated with the management and challenges of 'Big Data'.

methodsA list of recommendations was proposed for the reporting guideline based on the systematic review and consolidation of previously published data collection and reporting standards, including PhenX measures and Minimal Information about a Proteomics Experiment (MIAPE). Thereafter, these recommendations were reviewed by domain-specialists using an online survey, developed in Research Electronic Data Capture (REDCap). Following interpretation and consolidation of the survey results, the recommendations were mapped to existing ontologies using Zooma, Ontology Lookup Service and the Bioportal search engine. Additionally, an associated eXtensible Markup Language schema was created for the REDCap implementation to increase user friendliness and adoption.

resultsThe online survey was completed by 53 respondents; the majority of respondents were dual clinician-researchers (57%), based in Australia (35%), Africa (33%) and North America (22%). Data elements within the reporting standard were identified as participant-level, study-level and experiment-level information, further subdivided into essential or optional information.

conclusionThe reporting guideline is readily employable for kidney disease research projects, and also adaptable for clinical utility. The adoption of the reporting guideline in kidney disease research can increase data quality and the value for long-term preservation, ensuring researchers gain the maximum benefit from their collected and generated data.

Indexed as

Biomedical ResearchGuidelines as TopicHumansKidney DiseasesNephrologyReproducibility of ResultsResearch DesignTranslational Research, Biomedicaldata reportingdata standardisationFAIRH3ABioNetkidney diseasereporting guideline

Identifiers

PMID31772086
PMCPMC6887010

What OpenQuestion holds

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LicenceCC BY-NC
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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.