Urine Creatinine Excretion and Clinical Outcomes in CKD
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Twenty-four-hour urine creatinine excretion is a reliable approximation of muscle mass. Whether changes in urine creatinine predict clinical outcomes in persons with CKD is unknown. This work studied the relationship between urine creatinine and patient and renal survival in people with CKD not requiring renal replacement therapy. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This longitudinal cohort study included incident stages 3-5 CKD patients referred to the renal clinic at the University Federico II in Naples between January of 1995 and December of 2005. Clinical data and urine creatinine were updated at each visit. Main outcomes were all-cause mortality and kidney failure requiring dialysis. RESULTS: This study enrolled 525 individuals and followed them for a median of 6 years (range of 4 months to 15 years). Urine creatinine excretion declined by 16 mg/d per year (95% confidence interval, 14 to 19) in participants with CKD stages 3a, 3b, and 4, and it remained stable in participants with stage 5 CKD. Per each 20 mg/d decline in urine creatinine, mortality increased by 3% (adjusted hazard ratio, 1.03; 95% confidence interval, 1.01 to 1.05), and the risk of initiating dialysis increased by 2% (adjusted hazard ratio, 1.02; 95% confidence interval, 1.01 to 1.03). These associations were independent of body mass index and GFR. CONCLUSIONS: In persons with CKD stages 3 and 4, urine creatinine declines at a rate of 16 mg/d per year. Lower urine creatinine excretion predicts greater risk of kidney failure and patient mortality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".