The effect of implementing a dialysis start unit on modality decision among patients with unplanned start kidney replacement therapy
Bibliographic record
Abstract
INTRODUCTION: Many individuals start dialysis in an acute setting with suboptimal pre-dialysis education. These individuals are often treated with central venous catheter insertion and initiation of in-center hemodialysis and only a minority will transfer to a home-based therapy. The dialysis start unit is a program performing in-center hemodialysis in a separate space while providing support and education on chronic kidney disease and treatment options in the initial weeks of kidney replacement therapy. We aimed to assess the uptake of home dialysis therapies between 2013 and 2021 among patients who started acute inpatient hemodialysis at University Health Network, Toronto and underwent dialysis at the dialysis start unit. METHODS: This is a retrospective observational cohort study based on prospectively collected data. Patients' demographics were obtained from electronic charts. In the dialysis start unit, all patients received dialysis modality education by a nurse educator, dedicated home dialysis nurses, and the allied health care team. FINDINGS: During 2013-2021, 122 patients were dialyzed in the dialysis start unit and included in the study. Among those patients, 68 patients ultimately chose home dialysis (57 peritoneal dialysis and 11 home hemodialysis). Fifty-four patients continued in-center hemodialysis. Patients adopting home dialysis were less likely to have diabetes and hypertension as the etiology of kidney failure and more likely to have glomerulonephritis or vasculitis. DISCUSSION: Dialysis modality education is implementable in advanced chronic kidney disease. Individualized education and care after unplanned start dialysis can potentially enhance home dialysis choice and utilization.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".