The diabetes risk score: a practical tool to predict type 2 diabetes risk.

By J. Lindström, J. Tuomilehto. Diabetes Care, 2003

1,959 citations recorded by the source corpus.

Subfields: Endocrinology, Diabetes and Metabolism, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/dd6c3d9ffa23b8dc3b854ca78025499234a909d7

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In Endocrinology, Diabetes and Metabolism

In Molecular Biology

Citations in the corpus

Guidelines on cardiovascular disease prevention in clinical practice: the European perspective (2005, positive): “A simple DM risk questionnaire 2443 can guide which patients without CVD should be tested for DM.(402) 2444 Keeping close to the recommended targets for BP, lipid control, glycaemia, and HbA1c is 2445 important for the prevention of CVD.”
Risk Factors Contributing to Type 2 Diabetes and Recent Advances in the Treatment and Prevention (2014, methodology): “FINDRISC (Finnish Diabetes Risk Score) questionnaire, the most commonly used method [160], is designed to self-assess the risk on the basis of seven questions, which has a good validity in the prediction of future diabetes onset over a 10-year period [158].”
2016 European Guidelines on cardiovascular disease prevention in clinical practice (2016, background): “3a.8.5 Lipid-lowering therapy The Heart Protection Study (HPS) demonstrated that treatment with simvastatin 40 mg reduced the risk of CAD and stroke in people with DM and individuals without DM who had no prior AMI or angina pectoris.372 Further robust support for statin benefit came from the Collab”
2016 European Guidelines on cardiovascular disease prevention in clinical practice (2017, neutral): “tients without CVD should be tested for DM [400].”
2016 European Guidelines on cardiovascular disease prevention in clinical practice. (2016, background): “The use of insulin analogues, insulin pumps and continuous glucose monitoring to improve glycaemic control while minimizing hypoglycaemia is the subject of intense research, as is the use of agents (e.g. metformin, GLP-1 agonists) commonly used in type 2 DM.”
Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record (2018, neutral): “Most of these tools are not practical in the setting of the electronic health record (EHR) because they include predictor variables not readily available in structured formats [1-20].”
Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist (2014, background): “Prognostic models may involve models for individuals with a particular health condition, such as prediction of recurrence or death after diagnosis of breast cancer [8] or mortality after cardiac surgery [9], but also includes models for predicting the occurrence of future outcomes in apparently heal”
Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. (2007, methodology): “Investigators from Finland developed a diabetes risk score and predicted T2DM during 5 years of follow-up in a middle-aged population sample that identified cases by initiation of diabetes medications.(34) They found that age, BMI, waist circumference, history of blood pressure therapy, high blood g”