Return on Investment: An Economic Guideline for Selecting Home Daily/Nocturnal Hemodialysis Patients
Bibliographic record
Abstract
Starting any new program, especially one without a proven track record, raises questions about cost‐effectiveness of the treatment. Purpose: This research investigated how long patients should be expected to remain as daily/nocturnal hemodialysis patients in order to justify the initial investment in sending them home. Methods: Costs for 10 short‐hour daily (SHD) and 12 slow nocturnal hemodialysis (NHD) were compared with the savings incurred by switching those patients from conventional hemodialysis (CHD). One‐time expenses were divided by net savings to determine the minimum length of time the patients should be expected to remain at home on these modalities. Results: One‐time training, installation, and home equipment expenses were comparable for the SHD and NHD patients. NHD patients without monitoring noticed that these costs recovered in 1 year. NHD patients with monitoring took approximately 16 months to recover these costs, while initial SHD costs were offset in 20 months. Conclusions: Patients selected for home NHD and SHD should be expected to be able to remain at home for at least 12–20 months. Subsequent investigation indicates that these costs and time periods may be further reduced. NHD Costs ($Can) Daily (SHD) Excluding monitoring Including monitoring One‐time $21,281 $19,772 $21,465 Savings $12,836 $20,484 $16,703 ROI (years) 1.7 1.0 1.3
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".