System-Level Strategies to Improve Home Dialysis
Bibliographic record
Abstract
Advocacy and policy change are powerful levers to improve quality of care and better support patients on home dialysis. While the kidney community increasingly recognizes the value of home dialysis as an option for patients who prioritize independence and flexibility, only a minority of patients dialyze at home in the United States. Complex system-level factors have restricted further growth in home dialysis modalities, including limited infrastructure, insufficient staff for patient education and training, patient-specific barriers, and suboptimal physician expertise. In this article, we outline trends in home dialysis use, review our evolving understanding of what constitutes high-quality care for the home dialysis population (as well as how this can be measured), and discuss policy and advocacy efforts that continue to shape the care of US patients and compare them with experiences in other countries. We conclude by discussing future directions for quality and advocacy efforts.
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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 | 0.031 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".