Evidence map›Paper›PMID 41181531›Full record

ArticleWorld journal of gastrointestinal endoscopy2025

Postoperative perforation risk after endoscopic removal of gastric gastrointestinal stromal tumors: Development and validation of a predictive nomogram.

Dong-Mei Zhang, Pan-Pan Li, Min-Hui Zhang, Zong-Xian Zhao, Ya-Bo Zhou

Abstract read
In one paragraph

Article in World journal of gastrointestinal endoscopy, 2025. 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

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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. An interpretable machine learning model integrating [European journal of nuclear medicine and molecular imaging · 2026
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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

5 authors.

Dong-Mei ZhangDepartment of Gastroenterology, Fuyang People's Hospital Affiliated to Bengbu Medical University, Fuyang 236000, Anhui Province, China.
Pan-Pan LiDepartment of Gastroenterology, Fuyang People's Hospital Affiliated to Bengbu Medical University, Fuyang 236000, Anhui Province, China.
Min-Hui ZhangDepartment of Gastroenterology, Fuyang People's Hospital Affiliated to Bengbu Medical University, Fuyang 236000, Anhui Province, China.
Zong-Xian ZhaoDepartment of Anorectal Surgery, Fuyang People's Hospital Affiliated to Bengbu Medical University, Fuyang 236000, Anhui Province, China. 461901580@qq.com.
Ya-Bo ZhouDepartment of Gastroenterology, Fuyang People's Hospital Affiliated to Bengbu Medical University, Fuyang 236000, Anhui Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGastrointestinal stromal tumors are the most common mesenchymal tumors of the gastrointestinal tract, and gastric gastrointestinal stromal tumors (gGISTs) account for the majority of these tumors. Currently, endoscopic removal (ER) is increasingly adopted as a minimally invasive treatment. However, postoperative perforation remains a critical complication, necessitating robust prediction tools.

aimTo identify the risk factors and develop a validated nomogram for predicting perforation after ER of gGISTs.

methodsThis retrospective study analyzed the patients undergoing ER at Fuyang People's Hospital from 2019 to 2024. Clinical data, including tumor size, location, and procedural details, were collected and analyzed. The risk factors were identified

resultsAmong 301 patients, the perforation rate was 6.3%. Multivariate analysis identified tumor size (odds ratio = 4.699, 95% confidence interval: 2.382-9.267,

conclusionThis study identified that tumor size and location were independent risk factors, and provides a clinically actionable nomogram for evaluating and predicting postoperative perforation risk in gGISTs treated with ER, facilitating preprocedural planning and risk monitoring.

Indexed as

Endoscopic removalGastric gastrointestinal stromal tumorsLogistic regressionNomogramPerforationPredicting model

Identifiers

PMID41181531
PMCPMC12576693

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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.