ArticleBMC geriatrics2025
Construction and validation of an oral frailty risk prediction model for community-dwelling older adults.
Article in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for oral frailty in older adults: a scoping review.Frontiers in medicine · 2026Pooled it
- Diagnostic Prediction Models for Oral Frailty in Older Adults: A Systematic Review and Critical Appraisal.Patient preference and adherence · 2026Review
- Construction and validation of a risk prediction model for oral frailty in rural hypertensive patients.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveThe aim of this study was to explore the factors that influence oral frailty (OF) in community - dwelling older adults, and to construct and validate a risk prediction model for oral frailty.
methodThrough univariate and logistic regression analyses, we identified predictors of oral frailty in community-dwelling older adults. A nomogram was drawn based on the logistic regression results, and the predictive ability of the model was assessed by the area under the receiver operating characteristic curve (ROC), the Hosmer-Lemeshow (H-L) test, and calibration curves.
resultsVariables in the model included history of falls within one year, sarcopenia risk, oral health knowledge, oral health beliefs and oral health behaviours (P < 0.05). The model showed good discrimination; the AUC for the training, validation and test groups was 0.895 (95% CI = 0.854–0.937), 0.880 (95% CI = 0.816–0.944) and 0.835 (95% CI = 0.760–0.910), respectively; H-L test results were χ2 = 6.126(P = 0.633), χ2 = 7.475( P = 0.486), χ2 = 5.603(P = 0.692), respectively.The calibration ability of all three groups of models was good.
conclusionThe predictive nomogram model constructed in this study exhibits excellent discrimination and calibration capabilities. It can accurately and conveniently screen the elderly population with oral frailty in the community, providing a reference for healthcare professionals to conduct early identification and implement prevention and control measures.
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Registered trials
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.