Growing home dialysis: The Ontario Renal Network Home Dialysis Initiative 2012–2019
Bibliographic record
Abstract
The Ontario Renal Network (ORN), a provincial government agency in Ontario, Canada, launched an initiative in 2012 to increase home dialysis use province-wide. The initiative included a new modality-based funding formula, a standard mandatory informatics system, targets for prevalent home dialysis rates, the development of a 'network' of renal programmes with commitment to home dialysis and a culture of accountability with frequent meetings between ORN and each renal programme leadership to review their results. It also included funding of home dialysis coordinators, encouragement and funding of assisted peritoneal dialysis (PD), and support for catheter insertion and urgent start PD. Between 2012 and 2017, home dialysis use rose from 21.9% to 26.5% and then between 2017 and 2019 stabilised at 26% to 26.5%. Over 7 years, the absolute number of people on home dialysis increased 40% from 2222 to 3105, while the number on facility haemodialysis grew 11% from 7935 to 8767. PD prevalence rose from 16.6% to 20.9%, a relative increase of 25%. The initiative showed that a sustained multifaceted approach can increase home dialysis utilisation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".