The marginal cost of satellite versus in‐center hemodialysis
Bibliographic record
Abstract
BACKGROUND: Despite increasing numbers of patients receiving hemodialysis in satellite units (SHD), the economic aspects have not been widely explored. A cost analysis of SHD and in-center hemodialysis (ICHD) from a societal perspective was performed to establish the efficiencies associated with shifting resources and patients from ICHD to SHD. METHODS: Costs were classified as fixed or variable and placed into categories. The resources for operating a SHD unit are the sum of two components: total fixed costs (TFC) and average variable cost (AVC) times SHD patient volume (Q). Using the TFC of a specific-sized SHD unit and the difference in AVC between ICHD and SHD the number of patients needed (Q) in the SHD unit for financial viability was determined. The formula TFC = (AVC(ICHD) - AVC(SHD)) X Q was used to determine the number of patients (Q) needed in a specific-sized SHD unit such that the yearly cost of SHD treatment would be the same as ICHD treatment. RESULTS: Our results show that SHD fixed costs can be fully offset if the volume of SHD patients is seven per year in a six-station unit. SHD costs were lower for nursing and physician fees. Therefore, ICHD care variable costs were $11,374 more per patient year. SHD patients would also have lower travel costs, a mean cost saving of $12,364 per year. CONCLUSION: SHD can result in significant savings both to the health-care system and to patients. Using the cost categories and formula presented, the number of patients needed in a specific-sized satellite unit to realize cost savings was determined for our program. We found that these savings can offset the fixed investment needed to operate a SHD unit at modest patient volumes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".