ALGORITHM;
DATA ANALYSIS;
DATA BASE;
DATA EXTRACTION;
DATA SYNTHESIS;
DIABETES MELLITUS;
DIABETIC RETINOPATHY;
ELECTRONIC DATABASE;
HUMAN;
INTERNATIONAL CLASSIFICATION OF DISEASES;
LETTER;
PRIORITY JOURNAL;
QUALITY CONTROL;
RISK ASSESSMENT;
SECONDARY ANALYSIS;
DIABETES MELLITUS, TYPE 2;
DIABETIC RETINOPATHY;
FEMALE;
HUMANS;
MALE;
EID: 84881616978PISSN: 0012186XEISSN: 14320428Source Type: Journal DOI: 10.1007/s00125-013-2994-xDocument Type: Letter
The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjects
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Optimal strategy to identify incidence of diagnostic of diabetes using administrative data
19715586 10.1186/1471-2288-9-62
Asghari S, Courteau J, Carpentier AC, Vanasse A (2009) Optimal strategy to identify incidence of diagnostic of diabetes using administrative data. BMC Med Res Methodol 9:62
Diabetes in Ontario: Determination of prevalence and incidence using a validated administrative data algorithm
11874939 10.2337/diacare.25.3.512
Hux JE, Ivis F, Flintoft V, Bica A (2002) Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care 25:512-516
Validation of diabetic retinopathy and maculopathy diagnoses recorded in a UK primary care database
22357184 10.2337/dc11-2069
Martín-Merino E, Fortuny J, Rivero E, García- Rodríguez LA (2012) Validation of diabetic retinopathy and maculopathy diagnoses recorded in a UK primary care database. Diabetes Care 35:762-767