Implementation and Evaluation of a Risk-Based Approach to Guide Chronic Kidney Disease Care: Protocol for a Multiphase Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: Risk prediction tools are used in a variety of clinical settings to guide patient care, although their use in chronic kidney disease (CKD) care is limited. OBJECTIVES: To assess the association of a risk-based model of CKD care on patient care, satisfaction, outcomes, and cost. DESIGN: Mixed-methods with a pre-post design. SETTING: We will use mixed-methods and a pre-post design to evaluate use of the Kidney Failure Risk Equation (KFRE) to guide CKD care. The KFRE will be applied to patients currently followed in nephrology multidisciplinary CKD clinics in Alberta, as well as to new patients being considered for multidisciplinary care. PATIENTS: will be recommended care by a multidisciplinary team coordinated by a nurse clinician and nephrologist, with access to other multidisciplinary resources including dietitians, pharmacists, and social workers as required. MEASUREMENTS/METHODS: Focus groups and interviews will be conducted to qualitatively describe patient and provider perspectives of potential barriers and facilitators to implementation of the risk-based approach to CKD care. Patient and provider surveys will also be used to quantify patient and provider satisfaction before and after the intervention. Finally, administrative data will be used to evaluate the association between the risk-based approach to care and outcomes including health care resource use, frequency of testing, modality choice, and death. CONCLUSIONS: Use of a risk-based model of care has the potential to increase use of optimal treatments such as the use of home dialysis and preemptive kidney transplantation, while reducing costs and poor outcomes related to processes of care such as unnecessary laboratory testing; however, there is also potential for unintended consequences. Our mixed-methods approach will integrate perceptions and needs from key stakeholders (including patients with CKD, their families, and their providers) to guide implementation and ensure appropriate modifications.
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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 | 0.100 | 0.069 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.011 |
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".