Person-Centered Integrated Care for Mild to Moderate CKD and Multimorbidity: A Scoping Review
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) is increasing in prevalence and most patients with this condition also experience multimorbidity, defined as two or more co-existing chronic health conditions, with recent reviews suggesting this includes up to 99% of CKD patients. Care and management for these patients is complex and multifaceted. Person-centered integrated care (PC-IC) is an ideal approach to support this population, but evidence for developing and implementing PC-IC strategies for patients with mild to moderate CKD is currently lacking. Methods: We conducted this review in accordance with the JBI methodology for scoping reviews and used the PRISMA-ScR. We searched for publications and grey literature that discuss the care of adult patients with mild to moderate chronic kidney disease experiencing multimorbidity, who may be the beneficiaries of PC-IC strategies. The Rainbow Model of Integrated Care is a framework for different levels of integrated care interventions, including macro- (system), meso- (organizational and professional), and micro-levels (clinical), all of which were considered in this review. Results: Our search of published works collected 31812 entries. From this, 721 papers progressed to full text screening. We included 136 for data extraction which is ongoing. Our grey literature search yielded one paper. General themes for integrated care strategies implemented that were revealed in our search include: - Collaborative care between nephrology and other medical specialities - Collaborative care between nephrology and other allied healthcare professions - Pharmacist initiated medication education and titration - Health knowledge education interventions delivered by nurses - Self-management education - Streamlined referrals between nephrology and primary care - Electronic health record applications - “Tele-nephrology” Conclusion: Our diverse research team is in the process of completing data extraction with the goal of better understanding the breadth of evidence that exists for PC-IC in multi-morbid patients with CKD. With this information, we hope to create well developed, and patient centered models of care for this increasingly complex and diverse population. Funding: Government Support – Non-U.S.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".