Evidence map›Paper›PMID 41988581›Full record

ArticleFrontiers in public health2026

Occupational skin health risk among nurses: development and validation of a nomogram-based prediction model.

Yi Xu, Ting Lu, Lixiang Feng, Yingying Chen, Siyan Chen, Rongsheng Xiong

Abstract readMulticenter StudyValidation Study
In one paragraph

Article in Frontiers in public health, 2026. 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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1 · What the graph read from it

What it found

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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

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yi Xu *Department of Thoracic Surgery, Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People's Hospital of Guangxi Zhuang Autonomous Region), Guilin, Guangxi, China.
Ting Lu *Department of Community Health, Cancer Research and Specialist Centre, School of Medical Sciences, Universiti Sains Malaysia (USM), George Town, Penang, Malaysia.
Lixiang FengGuangxi Agricultural Engineering Vocational Technical College, Chongzuo, Guangxi, China.
Yingying ChenDepartment of Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Siyan ChenDay Chemotherapy Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Rongsheng XiongDepartment of Thoracic Surgery, Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People's Hospital of Guangxi Zhuang Autonomous Region), Guilin, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Occupational contact dermatitis (OCD) is a prevalent work-related skin condition among nurses and remains a significant occupational health issue due to its impact on well-being, productivity, and workforce sustainability. However, reliable tools for early risk stratification in this population are lacking. This study aimed to develop and validate a nomogram-based prediction model to estimate the individual risk of OCD among nurses. Methods: A multicenter cross-sectional survey was conducted among 2,852 nurses from 40 hospitals across China. Participants were randomly assigned to a training cohort ( Results: Nine predictors were independently associated with OCD: age, dermatitis history, glove type, glove-wearing hours, handwashing frequency during work, hospital level, hand-cream habit, baseline skin condition, and sleep duration. The model showed excellent discrimination (AUC = 0.925 in the training set; 0.931 in the validation set). Calibration curves demonstrated close agreement between predicted and observed risks. DCA indicated consistently higher net benefit compared with the "treat-all" and "treat-none" strategies across wide threshold probability ranges (0.01-0.98 in the training set; 0.02-0.96 in the validation set). The resulting nomogram provides an intuitive, point-based tool for individualized OCD risk prediction. Conclusion: A robust, well-validated prediction model and nomogram were developed to estimate OCD risk among nurses. This tool may support occupational health screening, early risk identification, and targeted preventive strategies in healthcare institutions.

Indexed as

Dermatitis, OccupationalNomogramsNursesOccupational HealthAdultChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedRisk AssessmentRisk Factorscalibrationdecision curve analysislogistic regressionnomogramnursesoccupational contact dermatitisoccupational healthpublic health

Identifiers

PMID41988581
PMCPMC13078389

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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.