SynthesisGeriatrics & gerontology international2026
Risk Prediction Model for Frailty in Older Chinese Patients With Type 2 Diabetes Mellitus: A Systematic Review.
Synthesis in Geriatrics & gerontology international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
aimsTo systematically evaluate frailty risk prediction models for older patients with diabetes in China, providing a reference for healthcare professionals in selecting or developing appropriate frailty risk prediction models for older patients with diabetes and offering evidence for the formulation of intervention strategies.
methodsA systematic search was conducted in PubMed, the Cochrane Library, Medline, Embase, Web of Science, China Biomedical Literature Database, China National Knowledge Infrastructure (CNKI), VIP Database, and Wanfang Database for studies related to frailty risk prediction models for older patients with diabetes. The search period covered from database inception to November 15, 2025. Data extraction and quality assessment were independently performed by two researchers using the Prediction Model Risk of Bias Assessment Tool (PROBAST) and a data extraction form.
resultsA total of 14 studies involving 25 models were included, with outcome event incidence rates ranging from 10.1% to 51.2%. The area under the receiver operating characteristic curve (AUC) of the models ranged from 0.703 to 0.975. All 14 studies showed good overall applicability but exhibited a high risk of bias. High-frequency predictors included age, polypharmacy, nutritional status, glycated hemoglobin, and ADL score.
conclusionsFrailty risk prediction models for older patients with diabetes in China demonstrate good discriminative ability and applicability, but have significant methodological flaws and a high risk of bias. Future studies should strictly follow reporting guidelines for risk prediction models to develop and evaluate frailty risk prediction models for older patients with diabetes in China, and validate their feasibility in clinical practice to provide high-quality evidence for clinical decision-making.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.