Evidence map›Paper›PMID 41656225›Full record

ArticleBMC geriatrics2026

Development and validation of a risk prediction model for oral frailty among Chinese older adults with type 2 diabetes mellitus.

Wenjuan Yang, Kangqin Cai, Cheng Zhang, Peimin Zhuang, Qin Pan, Qingfang Lin, Liping Yang, Juan He, Xia Zhang, Junna Wang and 3 more

Abstract readValidation Study
In one paragraph

Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

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0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
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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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

13 authors.

Wenjuan YangDepartment of Nephrology, Zigong First People's Hospital, Zigong, Sichuan, 643000, China.
Kangqin CaiDepartment of Nephrology, Zigong First People's Hospital, Zigong, Sichuan, 643000, China.
Cheng ZhangDepartment of emergency, Handan Central Hospital, Handan, Hebei, 056000, China.
Peimin ZhuangDepartment of Nephrology, Putian First People's Hospital, Putian, Fujian, 351100, China.
Qin PanDepartment of Nephrology, Zigong First People's Hospital, Zigong, Sichuan, 643000, China.
Qingfang LinDepartment of Nephrology, Zigong First People's Hospital, Zigong, Sichuan, 643000, China.
Liping YangDepartment of Nephrology, Zigong First People's Hospital, Zigong, Sichuan, 643000, China.
Juan HeDepartment of Nephrology, Zigong First People's Hospital, Zigong, Sichuan, 643000, China.
Xia ZhangDepartment of Nephrology, Zigong First People's Hospital, Zigong, Sichuan, 643000, China.
Junna WangDepartment of Nephrology, Zigong First People's Hospital, Zigong, Sichuan, 643000, China.
Daiying JingDepartment of Nephrology, Zigong First People's Hospital, Zigong, Sichuan, 643000, China.
Meng FangDepartment of Nephrology, Deyang People's Hospital, Deyang, 618000, China. 3533915662@qq.com.
Jiquan ZhangDepartment of Cardiovascular Medicine, ZigongFirst People's Hospital, Zigong, Sichuan, 643000, China. dyzhangjiquan@163.com.

Funding

Health Commission of Sichuan Province Medical Science and Technology Program 24WSXT107Zigong City Philosophy and Social Sciences Key Research Base Patient Safety Research Center Project HZAQ-2023-04
6 · The paper itself

Abstract

objectiveTo investigate the prevalence and associated factors of oral frailty in older adults with type 2 diabetes, establish a nomogram prediction model, develop an assessment tool for early screening and prevention of oral frailty in this population, and provide scientific basis for the formulation of personalized oral health management strategies.

methodsA convenience sampling method was used to select 533 older adults with type 2 diabetes mellitus who visited two tertiary-level general hospitals in Sichuan Province and one tertiary-level general hospital in Hebei Province from April 2024 to June 2025 as the study subjects. They were randomly divided into a training set and a validation set in a 7:3 ratio. A risk prediction model was constructed through analysis, and a nomogram was drawn. The Hosmer-Lemeshow (H-L) test and receiver operating characteristic (ROC) curve were used to evaluate the model’s goodness of fit and predictive performance, respectively. The constructed model was validated through 1,000 bootstrap sampling iterations to assess its predictive efficacy.

resultsA total of 533 older adults with type 2 diabetes were included in the final analysis. Among them, 246 patients (46.15%) exhibited symptoms of oral frailty. Multivariate logistic regression analysis revealed that age, type of chronic disease, duration of diabetes, HbA1c level, periodontitis, number of natural teeth, difficulty chewing hard foods, and swallowing disorders were associated with the risk of oral frailty in older adults with type 2 diabetes (all P < 0.05); The area under the ROC curve (AUC) of the predictive model was 0.847( 95%CI: 0.808–0.886), a sensitivity of 72.3%, a specificity of 83.5%, and a maximum Youden index of 0.558. Internal validation showed that the area under the ROC curve for the validation set was 0.831(95%CI:0.768–0.894), with a sensitivity of 82.2% and specificity of 72.4%. The Hosmer-Leme-show test yielded χ² = 13.548, P = 0.094 (both > 0.05). The calibration curve showed good agreement between the nomogram model and actual observed values. The ROC and DCA decision curves indicated that the nomogram model has good predictive performance and clinical utility.

conclusionsThe nomogram model developed in this study provides convenience for clinical assessment of the risk of oral frailty in older adults with type 2 diabetes, and helps doctors identify high-risk groups.

Indexed as

Diabetes Mellitus, Type 2FrailtyOral HealthAgedAged, 80 and overChinaEast Asian PeopleFemaleFrail ElderlyHumansMaleNomogramsRisk AssessmentRisk FactorsROC CurveOlder adultsOral frailtyPredictive modelType 2 diabetes mellitus

Identifiers

PMID41656225
PMCPMC12983867

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LicenceCC BY-NC-ND
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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.