Cost of Dialysis Therapy by Modality in Manitoba
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The prevalence of ESKD is increasing worldwide. Treating ESKD is disproportionately costly in comparison with its prevalence, mostly due to the direct cost of dialysis therapy. Here, we aim to provide a contemporary cost description of dialysis modalities, including facility-based hemodialysis, peritoneal dialysis, and home hemodialysis, provided with conventional dialysis machines and the NxStage System One. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We constructed a cost-minimization model from the perspective of the Canadian single-payer health care system including all costs related to dialysis care. The labor component of costs consisted of a breakdown of activity-based per patient direct labor requirements. Other costs were taken from statements of operations for the kidney program at Seven Oaks General Hospital (Winnipeg, Canada). All costs are reported in Canadian dollars. RESULTS: Annual maintenance expenses were estimated as $64,214 for in-center facility hemodialysis, $43,816 for home hemodialysis with the NxStage System One, $39,236 for home hemodialysis with conventional dialysis machines, and $38,658 for peritoneal dialysis. Training costs for in-center facility hemodialysis, home hemodialysis with the NxStage System One, home hemodialysis with conventional dialysis machines, and peritoneal dialysis are estimated as $0, $16,143, $24,379, and $7157, respectively. The threshold point to achieve cost neutrality was determined to be 9.7 months from in-center hemodialysis to home hemodialysis with the NxStage System One, 12.6 months from in-center hemodialysis to home hemodialysis with conventional dialysis machines, and 3.2 months from in-center hemodialysis to peritoneal dialysis. CONCLUSIONS: Home modalities have lower maintenance costs, and beyond a short time horizon, they are most cost efficient when considering their incremental training expenses. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2018_07_18_CJASNPodcast_18_8_F.mp3.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".