Protein energy wasting and long‐term outcomes in nondialysis dependent chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Nutritional status and protein energy wasting (PEW) is prevalent in patients with nondialysis-dependent chronic kidney disease (CKD). The relationship between PEW and long-term development of clinically important outcomes remains to be examined. OBJECTIVES: To investigate the relationships between PEW, as measured by Subjective Global Assessment (SGA 1-7), and progression to important clinical outcomes: mortality and/or kidney failure. DESIGN: Prospective cohort design. PARTICIPANTS: One hundred and thirty-nine participants were well-nourished and 37 moderately malnourished patients with CKD 3-5. MEASUREMENTS: The outcomes were 2, 5, and 10-year progression to kidney failure (dialysis or transplant) or mortality, kidney failure alone, and mortality alone. SGA was determined by a registered renal dietitian. Food frequency questionnaires were used to assess dietary intake. Clinical and laboratory baseline characteristics were collected. Multivariable regression models and Cox models were created to examine the relationship between SGA and outcomes. RESULTS: PEW was associated with the combined outcome of kidney failure or mortality at 2 (p = 0.003), 5 (p = 0.004), but not at 10 (p = 0.73) years. This relationship was primarily driven by the relationship between PEW and kidney failure. In Cox models, the relationship between PEW and kidney failure remained after adjusting for Kidney Failure Risk Equation scores. The multivariable modeling revealed that PEW remained a statistically significant predictor of the combined outcome and ESKD after adjustment for age, estimated glomerular filtration rate (eGFR), sex, albumin-to-creatinine ratio, diabetes, albumin, and protein intake. CONCLUSIONS: PEW, determined by the SGA 1-7, is an important prognostic tool. Further research looking at clinically important outcomes are needed to implement nutritional interventions for nondialysis-dependent CKD patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".