Screening to Prevent Polyoma Virus Nephropathy: A Medical Decision Analysis
Bibliographic record
Abstract
Polyomavirus nephropathy (PVN) is an emerging medical dilemma in kidney transplantation. Methods to screen before clinical disease are available and early immunosuppression reduction may change the natural history of progression. However, the consequences of an increase in rejection may limit the benefits. In a simulation model a 'screen' versus 'no-screen' strategy was compared. Baseline PVN cumulative incidence was assumed to be 4%. Patients with PVN were modeled to have 4-fold higher risk of graft loss. In the screen strategy, patients positive for blood DNA PCR had their immunosuppression reduced. This pre-emptive change was modeled to reduce progression to overt PVN by 80%. Therapy reduction was associated with a 10% risk of precipitating acute rejection and greater risk of chronic allograft loss. In the baseline case, screening saved 1912 dollars (discounted) and produced 0.020 more quality adjusted life years (QALYs) than not screening. Screening resulted in decreased net QALYs if the false positive viremia rate was >9.5% and the PVN incidence was <2.1%. Much of the cost savings of screening relate to savings from immunosuppression reduction in the screened arm. Screening may well be cost-effective if not cost saving in centers with high PVN rates. There remain significant areas of uncertainty.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".