Perceived Benefits and Challenges of a Risk-Based Approach to Multidisciplinary Chronic Kidney Disease Care: A Qualitative Descriptive Study
Bibliographic record
Abstract
BACKGROUND: The kidney failure risk equation (KFRE) provides an estimate of risk of progression to kidney failure, and may guide clinical care. OBJECTIVE: We aimed to describe patient, family, and health care provider's perspectives of the perceived benefits and challenges of using a risk-based approach to guide care delivery for patients with advanced chronic kidney disease (CKD), and refine implementation based on their input. METHODS: We used qualitative methodology to explore perceived benefits and challenges of implementing a risk-based approach (using the KFRE) to determine eligibility for multidisciplinary CKD care in Southern Alberta. We obtained perspectives from patients and families through focus groups, as well as input from health care providers through interviews and open-ended responses from an online survey. Twelve patients/family members participated in 2 focus groups, 16 health care providers participated in an interview, and 40 health care providers responded to the survey. RESULTS: Overall, participants felt that a KFRE-based approach had the potential to improve efficiency of the clinics by targeting care to patients at highest risk of kidney failure; however, they also expressed concerns about the impact of loss of services for lower risk individuals. Participants also articulated concerns about a perceived lack of capacity for adequate CKD patient care in the community. Our implementation strategy was modified as a result of participants' feedback. CONCLUSIONS: We identified benefits and challenges to implementation of a risk-based approach to guide care of patients with advanced CKD. Based on these results, our implementation strategy has been modified by removing the category of referral back to primary care alone, and instead having that decision made jointly by nephrologists and patients among low-risk 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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".