The Prevalence of Chronic Kidney Disease and Albuminuria in Patients With Type 1 and Type 2 Diabetes Attending a Single Centre
Bibliographic record
Abstract
Background Diabetes is the leading cause of chronic kidney disease worldwide. Diabetic kidney disease is one of the microvascular complications of diabetes and it involves changes in glomerular hemodynamics, interstitial fibrosis, and tubular atrophy. Early detection and management of Diabetic kidney disease (DKD) help to reduce morbidity and mortality. This study aims to assess the prevalence of nephropathy and albuminuria in the diabetic population attending an Irish tertiary care center. Methods Retrospective data collection and analysis of patients diagnosed with Type 1 diabetes mellitus (T1DM) and Type 2 diabetes mellitus (T2DM) through the Development and Integration of Accurate Mathematical Operations in Numerical Data-Processing (DIAMOND) database in a single Irish tertiary care center. An audit tool was used to collect patients' information including gender, age, type of diabetes, serum creatinine, urinary albumin excretion, albumin creatinine ratio (ACR), body mass index, and last available glycated hemoglobin (HbA1c). Results Out of 7394 subjects with T2DM, 3139 (42%) were identified with chronic kidney disease (CKD). There were 1866 subjects with positive ACR out of 3139 subjects with CKD in the T2DM cohort. This shows that 25% of subjects have diabetic kidney disease and 17% have CKD of undetermined etiology. In the T1DM cohort with 1166 subjects, 209 (18%) were identified with CKD. Out of these 209 subjects with CKD, 164 (14%) were ACR-positive. The prevalence of CKD and albuminuria were related to age in both T1DM and T2DM populations. Albuminuria showed a linear relationship with age in subjects with no known CKD, which shows that age causally relates to albuminuria independent of type and duration of diabetes. Conclusion CKD is more prevalent in patients with T2DM as compared to T1DM, whereas the prevalence of albuminuria is higher in the T1DM population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".