Estimating patient-borne water and electricity costs in home hemodialysis: a simulation
Bibliographic record
Abstract
BACKGROUND: Home hemodialysis is associated with lower costs to the health care system compared with conventional facility-based hemodialysis because of lower staffing and overhead costs, and by transferring the treatment cost of utilities (water and power) to the patient. The purpose of this study was to determine the utility costs of home hemodialysis and create a formula such that patients and renal programs can estimate the annual patient-borne costs involved with this type of treatment. METHODS: Seven common combinations of treatment duration and dialysate flows were replicated 5 times using various combinations of home hemodialysis and reverse osmosis machines. Real-time utility (electricity and water) consumption was monitored during these simulations. A generic formula was developed to allow patients and programs to calculate a more precise estimate of utility costs based on individual combinations of dialysis intensity, frequency and utility costs unique to any patient. RESULTS: Using typical 2014 utility costs for Edmonton, the most expensive prescription was for nocturnal home hemodialysis (8 h at 300 mL/min, 6 d/wk), which resulted in a utility cost of $1269 per year; the least expensive prescription was for conventional home hemodialysis (4 h at 500 mL/min, 3 d/wk), which cost $420 per year. Water consumption makes up most of this expense, with electricity accounting for only 12% of the cost. INTERPRETATION: We show that a substantial cost burden is transferred to the patient on home hemodialysis, which would otherwise be borne by the renal program.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".