The Actual Operative Costs of Liver Transplantation and Normothermic Machine Perfusion in a Canadian Setting
Bibliographic record
Abstract
BACKGROUND: Liver transplantation is an effective treatment for end-stage liver disease. However, waiting lists continue to lengthen as demand exceeds supply. Use of extended criteria donors has helped but is associated with increased rates of complications. The application of normothermic machine perfusion (NMP) has been shown to be protective, especially in more marginal grafts. Despite this benefit, no cost-effectiveness studies have been published. OBJECTIVE: This study serves as a prelude to a cost-effectiveness analysis of the costs of liver procurement, transplantation, and machine perfusion in a Canadian setting. METHODS: The total costs were calculated for 106 in-province procurements, the set cost for 237 out-of-province procurements, and 343 liver transplantations. These costs include overheads, supplies, anaesthesia technologist and nursing salaries, and physician billings. Base and modified costs for all procedures were calculated, with consideration of physician billing modifiers. The total cost per run of NMP was calculated, with a range based on variations in the exchange rates for Great British pounds (₤) to Canadian dollars ($Can), year 2019 values. RESULTS: Costs were $Can30,770.22 for in-province and $Can44,636.73 for out-of-province liver procurement and transplantation. These increased to $Can35,659.22 and 48,076.18 when considering modifiers. The minimum cost per NMP run was $Can18,593.02. CONCLUSIONS: Although the cost per run is substantial, NMP could potentially lead to cost savings by decreasing night-time salary premiums, complications, and patient length of stay. A formal cost-effectiveness study of NMP in liver transplantation is underway to help clarify the financial benefit or burden of this new technology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".