Screening to Prevent Polyoma Virus Nephropathy in Kidney Transplantation: A Cost Analysis
Bibliographic record
Abstract
Polyoma virus nephropathy is an important cause of graft dysfunction in kidney transplant recipients and screening to prevent disease has been advocated. Although screening incurs new costs, our hypothesis is that savings from less immunosuppression in those with positive screening tests could pay for overall costs of screening. In 134 consecutive recipients, polyoma virus (positive decoy cells) was detected in the urine of 34 (25.4%) individuals over a 2-year follow-up. Of these 34, 11 had a plasma BK PCR of >7700 copies/mL. Immunosuppression was reduced stepwise in these patients until viral loads fell <1000/mL. Overall screening costs (including extra plasma PCR testing) were estimated at $33,450. Those with positive PCR had greater reductions in annual immunosuppression costs by year 2 ($6452 vs. $2799, p = 0.0015) compared to those with negative screens. At the end of the 2-year period, 61% of the screening costs were covered by less immunosuppressant costs. At the end of 30 months there were net savings. In summary, reductions in immunosuppression cover the cost of screening for polyoma viral infection. Longer-term follow-up is needed to ensure patient outcomes remain acceptable.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".