Patient Navigators for CKD and Kidney Failure: A Systematic Review
Bibliographic record
Abstract
Background: To what degree and how patient navigators improve clinical outcomes for patients with chronic kidney disease (CKD) and kidney failure is uncertain. We performed a systematic to summarize patient navigator program design, evidence, and implementation in kidney disease. Methods: A search strategy was developed for randomized controlled trials and observational studies that evaluated the impact of navigators on outcomes in the setting of CKD and kidney failure. Articles were identified from various databases. Two reviewers independently screened the articles and identified those which met the inclusion criteria. Data was abstracted from full texts and risk of bias was assessed. Results: After screening a total of 3371 citations, 17 articles met the inclusion criteria including 14 original studies. Navigators came from various healthcare backgrounds including nursing (n=6), social worker (n=2), medical interpreter (n=1), research (n=1) and also included kidney transplant recipients (n=2) and non-medical individuals (n=2). Navigators focused mostly on education (n=9) and support (n=6). Navigators were used for patients with CKD (n=5), peritoneal dialysis (n=2), in-center hemodialysis (n=4), kidney transplantation (n=2) but not home hemodialysis. Navigators improved transplant workup and listing, adherence, peritoneal dialysis utilization and patient knowledge. However, many studies did not show benefits across other outcomes, were at a high risk of bias, and none reported cost effectiveness or patient reported experience measures. Conclusions: Navigators improve some health outcomes for CKD but there is heterogeneity in their structure and function. High-quality randomized controlled trials are needed to evaluate patient navigator program efficacy and cost effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".