Management of Chronic Kidney Disease and End-Stage Renal Disease in Diabetes
Bibliographic record
Abstract
@font-face { font-family: Arial Unicode MS; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0cm 0cm 0.0001pt; font-size: 12pt; font-family: Times New Roman; }.MsoChpDefault { font-size: 10pt; }div.WordSection1 { page: WordSection1; } Diabetic nephropathy occurs in 20-40% of patients with diabetes mellitus and is the leading cause of end-stage renal disease (ESRD) in North America. This review outlines the evidence-based approach to the management of progressive Chronic Kidney Disease (CKD) and ESRD in diabetes with the objective of guiding future physicians. In addition to patient education on diabetes management, vigilant annual screening for microalbuminuria and increased serum creatinine is the first step towards ensuring early treatment of CKD, well before the onset of frank proteinuria. In addition to controlling hyperglycemia, issues of hypertension and dyslipidemia should be addressed to prevent onset and progression of CKD, with Angiotensin-Converting Enzyme Inhibitors and Angiotensin II Receptor Blockers being the drugs of choice for controlling hypertension in these patients, and statins being the pharmacological mainstay for dyslipidemia. Furthermore, clinicians must address the consequences of CKD, particularly anemia, hyperphosphatemia, and vitamin D deficiency. Lifestyle modifications such as a low protein diet, smoking cessation, and cardiovascular and resistance exercises could help prevent progression and morbidity in CKD. When patients progress to irreversible kidney failure or ESRD, early (pre-emptive) transplantation before the initiation of dialysis has been shown to maximize survival. Owing to lower risk and better preservation of residual kidney function, peritoneal dialysis is now recommended as the initial modality of dialysis in most ESRD patients in the absence of a kidney transplant. Ultimately, effective management of kidney disease in diabetes relies on the collaborative efforts of the patient, their support system, and their multi-disciplinary healthcare team.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".