Prevalence-Based Targets Underestimate Home Dialysis Program Activity and Requirements for Growth
Bibliographic record
Abstract
BACKGROUND: Many renal programs have targets to increase home dialysis prevalence. Data from a large Canadian home dialysis program were analyzed to determine if home dialysis prevalence accurately reflects program activity and whether prevalence-based assessments adequately reflect the work required for program growth. METHODS: Data from home dialysis programs in British Columbia, Canada, were analyzed from 2005 to 2015. Prevalence data were compared to dialysis activity data including intakes and exits to describe program turnover. Using current attrition rates, recruitment rates needed to increase home dialysis prevalence proportions were identified. RESULTS: We analyzed 7,746 patient-years of peritoneal dialysis (PD) and 1,362 patient-years of home hemodialysis (HHD). The proportion of patients on home dialysis increased by 3.34% over the ten years examined, while the number of prevalent home dialysis patients increased 2.65% per year and the number of patients receiving home dialysis at any time in the year increased 4.04% per year. For every 1 patient net home dialysis growth, 13.6 new patients were recruited. Patient turnover included higher rates of transplantation in home dialysis than facility-based HD. Overall, the proportion dialyzing at home increased from 29.3 to 32.6%. CONCLUSIONS: There is high patient turnover in home dialysis such that program prevalence is an incomplete marker of total program activity. This turnover includes high rates of transplantation, which is a desirable interaction that affects home dialysis prevalence. The shortcomings of this commonly used metric are important for renal programs to consider, and better understanding of the activities that support home dialysis and the complex trajectories that home dialysis patients follow is needed.
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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) | 0.000 | 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".