"I'd Love It If Someone Gave Me Answers": Exploration of Patients' Perceptions of CKD and Kidney Failure Risk in the Context of Multimorbidity and Frailty
Bibliographic record
Abstract
Background: People living with chronic kidney disease (CKD) frequently experience multiple long-term conditions (multimorbidity) and/or frailty. Currently, this population is under researched and shared decision making about CKD treatment and kidney failure can be challenging. This study aims to explore patients’ knowledge of CKD/kidney failure, communication from healthcare professionals (HCPs) and perceptions of kidney failure risk. Methods: Semi-structured interviews with purposively sampled patients with a diagnosis of CKD and multimorbidity and/or frailty from primary and secondary care settings in the United Kingdom were conducted March-December 2024. An interview topic guide was developed based on the research questions, the Ottawa Decision Support Framework and the Ottawa Personal Decision Guide. Data were analysed by framework analysis, underpinned by Normalisation Process Theory (using Nvivo 14). Double coding and use of COREQ guidelines enhanced rigour. Results: Thirty-one participants, 46-94 years (mean 74), 52% male, 3-10 long-term conditions, clinical frailty scale 1-7, were interviewed. Five factors were found that influence the experience of understanding, perception and communication of risk: 1) Individuals knowledge and understanding of CKD and kidney failure; 2) The work required to live with CKD alongside multimorbidity and/or frailty; 3) Relationships and interactions with HCPs, the healthcare system and their support networks; 4) An individual’s context and priorities and 5) Uncertainty of health conditions/functional status and consideration of death. Conclusion: Many individuals were unaware of having a diagnosis of CKD and wanted to better understand CKD and kidney failure risk alongside other chronic health problems. Relationships and interactions between individuals, HCPs, healthcare systems and support networks are important, complex and vary by individual context/priorities. Findings aid knowledge of how an understanding of CKD and kidney failure risk can be embedded in the care of individuals with multimorbidity or frailty. Findings will inform care planning for important shared decision-making discussions about CKD and kidney failure in this group. Funding: Private Foundation Support
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".