Economic Evaluation of Screening for Polyomavirus Infection in Kidney Transplant Recipients: A Cost-Utility Analysis
Bibliographic record
Abstract
Screening for polyomavirus infection after kidney transplantation is recommended by clinical practice guidelines, but cost-effectiveness of this strategy is uncertain. The aim of this study was to estimate the incremental costs and benefits of routine screening for polyomavirus infection compared with no screening in kidney transplant recipients. Methods: Probabilistic Markov models were constructed to compare the health and economic benefits of routine screening for polyomavirus infection using real-time polymerase chain reaction assay. A series of 1-way and probabilistic sensitivity analyses were conducted to define the most influential variables in the model. Results: Monthly screening for 6 mo followed by 3 monthly screenings until 12 mo after transplant was dominant (lower costs and improved outcomes). Compared with no screening, the incremental benefits of screening were 0.294 life-years saved and 0.232 quality-adjusted life-years saved. Total savings from screening were $6986 Australian dollars ($5057 US dollars). The cost-effectiveness ratios were most sensitive to the costs of transplantation and dialysis, age of transplantation, prevalence of viremia, and probability of death in patients with a history of polyomavirus-associated nephropathy. Probabilistic sensitivity analysis indicated that screening (compared with no screening) was the dominant strategy across all plausible ranges of transition probabilities. Conclusions: Screening for polyomavirus infections 1 year following transplantation appears to save money, improves survival, and improves quality of life in kidney transplant recipients.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".