Treatment of Polyomavirus Infection in Kidney Transplant Recipients: A Systematic Review
Bibliographic record
Abstract
BACKGROUND.: Polyomavirus-associated nephropathy (PVAN) is an important cause of kidney graft loss but there is no consensus on its management. This study aimed to systematically document all published treatments for PVAN to determine the most effective therapy. METHODS.: A computerized search in MEDLINE, EMBASE, and Cochrane databases (1950-2008) was performed. References from review articles and published abstracts from the American Transplant Congress (2005-2008) were also included. Study selection criteria included (a) population: adult (>18 years) kidney-only, primary or repeat renal transplant recipients; (b) setting: polyoma viruria, viremia or biopsy-proven PVAN or both; and (c) treatment: immunosuppression reduction alone or with adjuvant agents. The primary outcome was graft failure rate, and secondary outcomes included acute rejection rate, elimination of viruria and viremia, graft function, patient survival, and adverse events. RESULTS.: Of 555 identified citations, 40 studies examining the effect of immunosuppression reduction alone or in combination with cidofovir, leflunomide, intravenous immunoglobulin, or ciprofloxacin were included for appraisal. Pooled results found a death-censored graft loss rate of 8/100 patient-years for immunosuppression reduction alone and 8 and 13/100 patient-years for the addition of cidofovir or leflunomide, respectively. CONCLUSIONS.: There does not seem to be a graft survival benefit of adding cidofovir or leflunomide to immunosuppression reduction for the management of PVAN. However, the evidence base is poor and highlights the urgent need for adequately powered randomized trials to define the optimal treatment of this important condition.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".