Evidence map›Paper›PMID 33311975›Full record

ArticleBiomarker insights2020

A Paradigm to Discover Biomarkers Associated With Chronic Kidney Disease Progression.

Ibrahim Ali, Sara T Ibrahim, Rajkumar Chinnadurai, Darren Green, Maarten Taal, Tony D Whetton, Philip A Kalra

Open access · goldAbstract read
In one paragraph

Article in Biomarker insights, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.1field-weighted citation impact, top 47% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. 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

7 authors at 4 institutions in 2 countries.

Ibrahim AliDepartment of Renal Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK.ORCID https://orcid.org/0000-0001-7328-2529
Sara T IbrahimInternal Medicine and Nephrology Department, University of Alexandria, Alexandria, Egypt.ORCID https://orcid.org/0000-0001-7442-7902
Rajkumar ChinnaduraiDepartment of Renal Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK.
Darren GreenDepartment of Acute Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK.
Maarten TaalFaculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK.
Tony D WhettonDivision of Cancer Sciences, University of Manchester, Manchester, UK.
Philip A KalraDepartment of Renal Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK.
Salford Royal NHS Foundation Trust · GBAlexandria University · EGUniversity of Manchester · GBUniversity of Nottingham · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biomarker discovery in the field of risk prediction in chronic kidney disease (CKD) embraces the prospect of improving our ability to risk stratify future adverse outcomes and thereby guide patient care in a new era of personalised medicine. However, many studies that report biomarkers predictive of CKD progression share a key methodological limitation: failure to characterise patients' renal progression precisely. This weakens any observable association between a biomarker and an outcome poorly defined by a patient's change in renal function over time. In this commentary, we discuss the need for a better approach in this research arena and describe a compelling strategy that has the advantage of offering robust and meaningful biomarker exploration relevant to CKD progression.

Indexed as

biomarkersChronic kidney diseasemethodologyprogression

Identifiers

PMID33311975
PMCPMC7716058
OpenAlexW3107478993

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.