Evidence map›Paper›PMID 36675488›Full record

ReviewJournal of clinical medicine2023

Prediction Models of Primary Membranous Nephropathy: A Systematic Review and Meta-Analysis.

Chanyu Geng, Liming Huang, Yi Li, Amanda Ying Wang, Guisen Li, Yunlin Feng

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PubMed Full text at PMC Publisher
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Review in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

6 authors.

Chanyu GengDepartment of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China.
Liming HuangDepartment of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China.
Yi LiDepartment of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China.
Amanda Ying WangThe Faculty of Medicine and Health Sciences, Macquarie University, Sydney 2109, Australia.
Guisen LiDepartment of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China.ORCID 0000-0003-1970-9979
Yunlin FengDepartment of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China.ORCID 0000-0003-4393-6661

Funding

Australian Government Research Training Program ScholarshipClinical research grant of Sichuan Provincial People's Hospital 2020LY02
6 · The paper itself

Abstract

backgroundSeveral statistical models for predicting prognosis of primary membranous nephropathy (PMN) have been proposed, most of which have not been as widely accepted in clinical practice.

methodsA systematic search was performed in MEDLINE and EMBASE. English studies that developed any prediction models including two or more than two predictive variables were eligible for inclusion. The study population was limited to adult patients with pathologically confirmed PMN. The outcomes in eligible studies should be events relevant to prognosis of PMN, either disease progression or response profile after treatments. The risk of bias was assessed according to the PROBAST.

resultsIn all, eight studies with 1237 patients were included. The pooled AUC value of the seven studies with renal function deterioration and/or ESRD as the predicted outcomes was 0.88 (95% CI: 0.85 to 0.90; I

conclusionsThe reported discrimination ability of included models was good; however, the insufficient calibration assessment and lack of validation studies precluded drawing a definitive conclusion on the performance of these prediction models. High-grade evidence from well-designed studies is needed in this field.

Indexed as

meta-analysisprediction modelsprimary membranous nephropathysystematic review

Identifiers

PMID36675488
PMCPMC9867146

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