Personal Support Worker (PSW)‐supported home hemodialysis: A paradigm shift
Bibliographic record
Abstract
INTRODUCTION: Despite improving clinical outcomes associated with the use of home hemodialysis (HD), its utilization is low in most countries. The inability or unwillingness of patients and their families to participate in their own treatment is one of the most important barriers to the adoption of home HD. METHODS: We hypothesized that paid helper-delivered home HD supported by public funds would be successful and welcomed by patients and be delivered at an affordable cost. We conducted a pilot project to dialyze six patients at home using Personal Support Workers (PSW) and resolve regulatory, organizational and financial constraints. FINDINGS: cWe provided publically-funded PSW-supported home HD to six patients. We describe the administrative structure of the pilot project allowing scalability and turnkey operation in the province of Ontario. Regulatory and insurance concerns were resolved and patients and staff were enthusiastic. The projected total dialysis cost, when economies of scale are met, are expected to be lower than the cost of in-center HD. DISCUSSION: A second phase of the project is currently under way including 8 hospitals and 67 patients. If equally successful, it may have significant implications for the delivery of care for End Stage Renal Disease in Ontario and similar jurisdictions. It promises to increase the utilization of home dialysis possibly at a lower cost than in-center HD. This would be particularly important in providing dialysis in underserviced and geographically hard to access areas.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".