Evidence map›Paper›PMID 41367508›Full record

ArticleTranslational pediatrics2025

Prediction of duodenal ulcers in children with

Siyuan Zheng, Chuchu Zheng, Shijie Cai, Xiaoyu Ge, Yan Chen, Weijie Ou, Yijun Lin

Abstract read
In one paragraph

Article in Translational pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

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

7 authors.

Siyuan ZhengThe School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
Chuchu ZhengThe School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
Shijie CaiThe School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
Xiaoyu GeThe School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
Yan ChenDepartment of Pediatrics, Fujian Children's Hospital, Fuzhou, China.
Weijie OuDepartment of Pediatrics, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Yijun LinDepartment of Pediatrics, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Methods: A total of 456 children with HP infection admitted to Fujian Children's Hospital and Fujian Maternal and Child Health Hospital were retrospectively included. The training group comprised 307 patients from Fujian Children's Hospital, while the validation group consisted of 149 patients from Fujian Maternal and Child Health Hospital. The patients in the training group were categorized into the HP infection group or HP infection with DU group. Firstly, least absolute shrinkage and selection operator regression analysis was conducted, and the selected variables were subjected to logistic regression analysis to identify the predictors of DUs in children with HP infection. Finally, a nomogram was established for the model. Results: Sex, melena, hemoglobin level, and uric acid level were included in the final prediction model. The area under curve values for the internal training and external validation groups were 0.740 and 0.728, respectively. The Hosmer-Lemeshow tests and calibration curves indicated that the degree of fit and consistency of the model were satisfactory. These decision curve analyses indicated that the model has certain clinical application value. Conclusions: If male children with melena, decreased hemoglobin levels and high uric acid levels are clinically encountered, DUs require high vigilance, and the children should be promptly referred to a facility with pediatric endoscopic capabilities for further diagnosis and treatment.

Indexed as

Childrenduodenal ulcer (DU)Helicobacter pylori (HP)nomogramprediction model

Identifiers

PMID41367508
PMCPMC12683415

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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.