Evidence map›Paper›PMID 41446859›Full record

ArticleFrontiers in medicine2025

A study on predictive nomogram for abdominal wall hernia in peritoneal dialysis patients based on multicenter data.

Yugang Cao, Tao Fang, Jun Guo, Sha-Sha Peng, Jin Yang

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. 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
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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.

No citing paper in PubMed yet.

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.

Yugang CaoHubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Department of Hepatobiliary Surgery, Huangshi Central Hospital, Hubei Polytechnic University School of Medicine, Huangshi, Hubei, China.
Tao FangHubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Department of Hepatobiliary Surgery, Huangshi Central Hospital, Hubei Polytechnic University School of Medicine, Huangshi, Hubei, China.
Jun GuoHubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Department of Hepatobiliary Surgery, Huangshi Central Hospital, Hubei Polytechnic University School of Medicine, Huangshi, Hubei, China.
Sha-Sha PengHubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Department of Hepatobiliary Surgery, Huangshi Central Hospital, Hubei Polytechnic University School of Medicine, Huangshi, Hubei, China.
Jin YangHubei Key Laboratory for Kidney Disease Pathogenesis and Intervention, Department of Hepatobiliary Surgery, Huangshi Central Hospital, Hubei Polytechnic University School of Medicine, Huangshi, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aims to develop and externally validate a scoring nomogram based on three key indicators (Peritoneal dialysis vintage, age, and albumin) to predict the risk of developing abdominal wall hernia in patients with end-stage renal disease (ESRD) undergoing peritoneal dialysis. Methods: A total of 480 patients undergoing peritoneal dialysis from three medical centers were enrolled in this study and randomly assigned into a training cohort ( Results: Gender, BMI, hemoglobin, ultrafiltration volume, diabetes, serum creatinine, and catheter insertion method showed no significant association with hernia development. PD vintage ( Conclusion: This study successfully developed and externally validated a novel nomogram with high accuracy that can effectively predict the risk of abdominal wall hernia in patients with end-stage renal disease undergoing peritoneal dialysis.

Indexed as

abdominal wall hernia incidenceend-stage renal diseasenomogramperitoneal dialysisprediction

Identifiers

PMID41446859
PMCPMC12722950

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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.