Identifying and slowing progressive chronic renal failure.
Bibliographic record
Abstract
OBJECTIVE: To help inform primary care physicians about how to identify and slow progressive chronic renal failure. QUALITY OF EVIDENCE: The National Library of Medicine (1996 to 2000) was searched using PubMed with search terms pertinent to studies on identification, course, and management of chronic renal failure. References in retrieved papers and older literature known to the authors supplemented the searches. In general, sufficient high-quality studies, systematic reviews, or guidelines based on such evidence were available to support our main points. MAIN MESSAGE: End-stage renal disease (ESRD) poses a large and growing morbidity, mortality, and financial burden. Almost all patients reach ESRD as a result of chronic progressive conditions, particularly diabetic nephropathy, hypertensive-vascular renal disease, and glomerular disorders. Patients at risk merit regular renal assessment with serum creatinine tests and urinalysis. Persistent high blood pressure and heavy proteinuria are the strongest predictors of progression of chronic renal failure. Patients with renal disease should be examined and treated for vascular disease and vice versa. Blood pressure lowering, ACE inhibition, and avoidance of further renal insults (such as use of nephrotoxins) can slow the decline of renal function. Restricting dietary protein has a weak effect on slowing renal failure and is not easy to apply in primary care. Timely involvement of specialized nephrology teams is important. CONCLUSION: Family physicians play an important role in recognizing patients with potential for renal failure, in demonstrating progressive chronic renal failure, and in initiating therapy early to improve outcomes.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".