Macroalbuminuria and Renal Pathology in First Nation Youth With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of macroalbuminuria and to describe the clinical and renal pathological changes associated with macroalbuminuria in a population of Canadian First Nation children and adolescents with type 2 diabetes. RESEARCH DESIGN AND METHODS: We conducted a retrospective chart review at a single tertiary care pediatric diabetes center, and a case series was constructed. We collected data on microalbuminuria (>or=3 mg/mmol creatinine [26.5 mg/g]) and macroalbuminuria (>or=28 mg/mmol creatinine [247.5 mg/g]), estimated glomerular filtration rate, renal pathology, and aggravating risk factors (poor glycemic control, obesity, hypertension, glomerular hyperfiltration, hypercholesterolemia, smoking, and exposure to diabetes in utero). RESULTS: We reviewed 90 charts of children and adolescents with type 2 diabetes. A total of 53% had at least one random urine albumin-to-creatinine ratio >or=3 mg/mmol (26.5 mg/g). There were 14 of 90 (16%) who had persistent macroalbuminuria at or within 8 years of diagnosis of diabetes. Of these 14 subjects, 1 had orthostatic albuminuria and 3 had spontaneous resolution of albuminuria. A total of 10 had renal biopsies performed. There were 9 of 10 who exhibited immune complex disease or glomerulosclerosis, and none had classic diabetic nephropathy. CONCLUSIONS: This study suggests that the diagnosis of renal disease in children with type 2 diabetes cannot be reliably determined by clinical and laboratory findings alone. Renal biopsy is necessary for accurate diagnosis of renal disease in children and adolescents with type 2 diabetes and macroalbuminuria. The additional burden of nondiabetic kidney disease may explain the high rate of progression to end-stage kidney failure in this 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".