Models of Care for the Implementation of Genetic Testing in Nephrology
Bibliographic record
Abstract
Genetic testing holds great potential to enhance the diagnosis and management of kidney disease, yet its integration into routine nephrology care remains limited and often delayed. Despite strong evidence supporting its clinical utility and cost effectiveness, significant barriers hinder its widespread adoption. This review examines care models designed to embed genetic testing into nephrology practice and proposes strategies to improve access for chronic kidney disease patients. Key approaches include enhancing clinical genetic services, establishing kidney genetics clinics, using technology such as virtual consultations, forming variant review boards and multidisciplinary teams, and mainstreaming genetic testing into nephrology care. For each model, the review identifies essential components for success and discusses barriers and facilitators to implementation. By focusing on practical, scalable, and patient-centered solutions, this review advocates for a paradigm shift in nephrology care. It envisions genetic testing as a standard component of kidney disease management, aiming to improve outcomes and promote equitable care for patients globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".