Assessing physical function in chronic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: People with chronic kidney disease have a high prevalence of poor physical function, which in turn is associated with poor health-related quality of life, and an increased risk of adverse events, including hospitalizations and all-cause mortality. Implementing early interventions may prove to be effective for preventing decline in physical function; however, it is imperative that clinicians screen patients to identify those at the highest risk of decline. In this review, we present subjective and objective screening tools that can easily and cost-effectively be implemented into routine nephrology practice to assess physical function. RECENT FINDINGS: Physical function can be assessed using commonly used physical performance tests that include objective measures, such as tests measuring gait speed, balance, chair-stand ability, and handgrip strength, as well as tests that include subjective self-reported measures. SUMMARY: The validated tools summarized in this review offer clinicians the ability to identify people at risk of poor physical function, in turn affording the opportunity to implement interventions for optimum management of risk of physical decline, preventing adverse health outcomes, and encouraging independence.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".