Evidence map›Paper›PMID 40523820›Full record

ArticleHua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology2025

[Risk prediction of demoralization syndrome in patients with oral cancer].

Liyan Mao, Xixi Yang, Xiaoqin Bi, Min Liu, Chongyang Zhao, Zuozhen Wen

Abstract readEnglish Abstract
In one paragraph

Article in Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Liyan MaoWest China School of Nursing, Sichuan University, Chengdu 610041, China.
Xixi YangDept. of Stomatology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
Xiaoqin BiState Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Dept. of Orthognathic and Temporomandibular Joint Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China.
Min LiuDept. of Orthopedic Surgery, West China Hospital, Sichuan University, Chengdu 610041, China.
Chongyang ZhaoDept. of Evidence-based Medicine and Clinical Epidemiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
Zuozhen WenDept. of Stomatology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to construct a risk prediction model for the occurrence of the demora-lization syndrome in patients with oral cancer and provide a scientific basis for the prevention of this syndrome in patients with oral cancer and the development of personalized care programs.

methodsA total of 486 patients with oral cancer in West China Hospital of Stomatology of Sichuan University and Sun Yat-sen Memorial Hospital of Sun Yat-sen University from 2024 March to July were selected by convenience sampling. We integrated clinical data and evidence from previous studies to identify the key variables affecting the demoralization syndrome in patients with oral cancer. The 486 patients were divided into a training set and a validation set in an 8∶2 ratio. A clinical risk prediction model was established based on the individual data of 365 patients in the development cohort. Through least absolute shrinkage and selection operator (LASSO) regression, a moderate to severe risk prediction model of demoralization syndrome in oral cancer was constructed, and a clinical machine-learning nomogram was constructed. Bootstrap resampling was used for internal validation. The data of 121 patients in the validation cohort were externally validated.

resultsThe incidence of the demoralization syndrome in patients with oral cancer was 405 cases (83.3%), of which 279 cases (57.4%) were mild, 176 cases (36.2%) were moderate, and 31 cases (6.4%) were severe. The core model, including patient education level, disease understanding, and MDASI-HN score, was used to predict the risk of outcome. Internal validation of the model yielded C statistic of 0.783 6 (95% CI: 0.78-0.87), beta of 0.843 4, and calibration intercept of -0.040 6. Through external validation, the validation set C statistic was 0.80 (95%CI: 0.71-0.87), beta was 0.80, and calibration intercept was -0.08.

conclusionsOur risk prediction mo-del of the demoralization syndrome in patients with oral cancer performed robustly in validation cohorts of different nur-sing environments. The model has good correction and good discrimination and can be used as an evaluation and prediction item at admission.

Indexed as

Mouth NeoplasmsAdultAgedFemaleHumansMachine LearningMaleMiddle AgedNomogramsRisk AssessmentRisk FactorsSyndromedemoralization syndromemachine learningoral cancerprediction model

Identifiers

PMID40523820
PMCPMC12211499

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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.