ArticleDiabetes technology & therapeutics2022
A Risk Stratification Approach to Allocating Diabetes Education and Support Services.
Article in Diabetes technology & therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed, 3 citations in OpenAlex.
- Development and validation of an explainable machine learning model for differentiating diabetic nephropathy from diabetic retinopathy in patients with type 2 diabetes.Frontiers in endocrinology · 2026Article
- Classification of self-care patterns in Korean adults with prediabetes using unsupervised machine learning: a secondary data analysis.Journal of Korean biological nursing science · 2025Article
- Association between Geriatric Nutrition Risk Index and depression in older hemodialysis patients with and without type 2 diabetes mellitus: a multicenter cross-sectional study.Frontiers in endocrinology · 2025Article
- Disentangling Population Health Management Initiatives in Diabetes Care: A Scoping Review.International journal of integrated careArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
The objective of this study was to describe a predictive modeling approach to risk stratify people with type 2 diabetes for diabetes self-management education and support (DSMES) services. With data from a large health system, a predictive model including age, glycated hemoglobin (HbA1c), and insulin use among other factors, was developed to assess risk of future high HbA1c. The model was retrospectively applied to a cohort of people who received DSMES over a 2-year period to assess the impact of DSMES on glycemia by risk strata. Of 6934 eligible people, 4014 (58%) were in the composite low-risk group and 2604 (38%) were in the composite high-risk group. Mean HbA1c change after DSMES was -0.38% in the low-risk group and -0.84% in the high-risk group. This analysis demonstrates the potential application of predictive modeling as one approach to target DSMES resources to people who will benefit most.
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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.