Cost-utility analysis of normothermic machine perfusion compared to static cold storage in liver transplantation in the Canadian setting
Bibliographic record
Abstract
To estimate the incremental cost-effectiveness of a liver transplant program that utilizes normothermic machine perfusion (NMP) alongside static cold storage (SCS) compared to SCS alone (control). A Markov model compared strategies (NMP vs. control) using 1-year cycle lengths over a 5-year time horizon from the public healthcare payer perspective. Primary micro-costing data from a single center retrospective trial were applied along with utility values from literature sources. Transition probabilities were deduced using the retrospective trial cohort, local transplant data, and supplemented with literature values. Scenario and probabilistic sensitivity analysis (PSA) were conducted. The NMP strategy was cost-effective in comparison to the control strategy, which was dominated. The mean cost for NMP was $456 455 (2021 US$) and the control was $519 222. The NMP strategy had greater incremental quality-adjusted life years (QALYs) gains over 5 years compared to the control, with 3.48 versus 3.17, respectively. The overarching results remained unchanged in scenario analysis. In PSA, NMP was cost-effective in 63% of iterations at a willingness-to-pay threshold of $40 941. The addition of NMP to a liver transplant program results in greater QALY gains and is cost-effective from the public healthcare payer perspective.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".