The assessment of frailty in older people with chronic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Frailty is common in chronic kidney disease (CKD) and is a predictor of adverse outcomes. The current article reviews the most common frailty measures available, gives an overview of their use in the chronic kidney disease population, and summarizes their strengths and limitations. RECENT FINDINGS: Frailty is increasingly recognized as a potent predictor of adverse outcomes in all stages of chronic kidney disease. Recent investigations have demonstrated that the clinical perception of frailty by healthcare personnel or patients themselves is an inaccurate measure of frailty. The clinical frailty scale, a simple point-of-care tool for the assessment of frailty, has been shown to be a predictor of mortality in individuals on dialysis. SUMMARY: The Fried criteria have been most extensively used in chronic kidney disease. However, other criteria using self-reported outcomes, clinical and cognitive criteria have also been shown to predict adverse outcomes and may be more applicable in clinical settings. Many of these still require further validation in the chronic kidney disease population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".