The Routine Use of High-Resolution Immunological Screening of Recipients of Primary Deceased Donor Kidney Allografts Is Cost-Effective
Bibliographic record
Abstract
BACKGROUND: The economic and health benefits of kidney transplantation are dependent on the length of allograft survival. High-resolution immunological screening can identify recipients at increased risk of early graft loss caused by acute rejection, but the use of these tests increases screening costs before transplantation. The objective of this study was to evaluate the cost-effectiveness of routine use of high-resolution flow-cytometry cross-matching and solid-phase screening for all recipients of primary deceased donor kidney transplants. METHODS: A Markov model was constructed to evaluate costs and effects of two different clinical strategies on a simulated cohort of 1,000 transplant recipients: serological screening (SS) only and flow screening (FS) only. Outcomes measures were total cost of patient care over 25 years, life expectancy, quality-adjusted life expectancy, and transplant life expectancy. RESULTS: In the base-case analysis, FS was associated with an average gain of 0.08 life years, 0.25 transplant life years, and 0.08 quality-adjusted life years per patient. SS was associated with a higher cost of CND$6,397 per patient, mostly because of increased use of dialysis in patients who suffered early graft loss under the SS strategy. The results were robust to uncertainty in the majority of variables, and a strategy using FS was cost-effective except under the unlikely scenario where the false-negative rate for SS was <or=2% or the early graft loss rate for flow-positive recipients was
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".