A Qualitative Study Describing the Perspectives on Frailty and Its Management in Individuals with Kidney Failure
Bibliographic record
Abstract
Key Points The term, frailty, had unclear meaning for most participants but was commonly explained as weakness, dependence, and unmodifiable. Knowledge of frailty assessment tools and the evidence to support their prognostic utility was low among clinicians. Though patients and caregivers saw value in discussing frailty, the label of frailty was often viewed as pejorative. Background Frailty is highly prevalent among individuals with kidney failure and independently associated with poor health outcomes. Identifying and managing frailty can inform prognosis and care but stakeholders' understanding of frailty and their perspectives on how to detect and manage it in routine kidney care are unknown. Methods We recruited participants from four Canadian kidney programs in Alberta, Manitoba, and Nova Scotia from January 2021 to June 2023. We conducted focus groups and semistructured interviews with patients (50 years or older with dialysis-dependent or nondependent kidney failure), caregivers, allied health care professionals, and nephrologists. We used qualitative description and inductive thematic analysis to describe their perspectives. Results Ninety-one people participated: patients ( N =31), caregivers ( N =8), kidney allied health care professionals ( N =38), and nephrologists ( N =14). We identified three themes, each with subthemes: ( 1 ) What is frailty? All groups expressed uncertainty, but frailty was commonly described as physical, visible, inevitable, and fixed; ( 2 ) discussing frailty: the value of knowing what to expect with frailty, and frailty as a difficult topic to discuss; ( 3 ) frailty assessment and management: skepticism from patients and caregivers that frailty is measurable; support from clinicians for a systematic approach to identifying frailty but a lack of knowledge on multidisciplinary roles and potential interventions. For all groups, having actionable solutions after identifying frailty was key for acceptability and successful implementation. Conclusions Education on the nature and potentially modifiable aspects of frailty as well as the scope and potential benefits of frailty interventions is necessary for successful implementation of frailty detection and management in kidney care.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".