Diabetic nephropathy. Prevention and early referral.
Bibliographic record
Abstract
OBJECTIVE: To review the clinical and pathophysiologic features of diabetic nephropathy and to examine evidence supporting primary, secondary, and tertiary treatment strategies. QUALITY OF EVIDENCE: The medical literature provides both level 1 and level 2 evidence on treatment of diabetic nephropathy, including randomized controlled trials, well-designed clinical trials without randomization, consensus papers, and cohort and case-control analytic studies. MAIN MESSAGE: Diabetes is the most common cause of end-stage renal failure in Canada and the United States, and both diabetes and its renal complications are increasing. Diabetic nephropathy, in both type 1 and type 2 diabetes, usually progresses through five stages. Treatment and prevention strategies depend on stage of disease. Primary prevention includes addressing hyperglycemia, hypertension, and smoking. Secondary prevention adds angiotensin-converting enzyme inhibitors, cholesterol lowering, and perhaps restrictions on dietary protein. Tertiary care, including dialysis or transplantation, is generally managed by nephrologists, but family physicians continue to play an important role in the care of these patients. CONCLUSIONS: Diabetic nephropathy is a serious cause of morbidity and mortality for patients with type 1 and type 2 diabetes. To reduce end-stage diabetic nephropathy and its complications, both specialists and family physicians need to focus efforts on primary and secondary prevention strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".