Considerations for Screening Live Kidney Donors for Endemic Infections: A Viewpoint on the UNOS Policy
Bibliographic record
Abstract
In February 2013, the Organ Procurement and Transplantation Network mandated that transplant centers perform screening of living kidney donors prior to transplantation for Strongyloides, Trypanosoma cruzi and West Nile virus (WNV) infection if the donor is from an endemic area. However, specific guidelines for screening were not provided, such as the optimal testing modalities, timing of screening prior to donation and the appropriate selection of donors. In this regard, the American Society of Transplantation Infectious Diseases Community of Practice, together with disease-specific experts, has developed this viewpoint document to provide guidance for the testing of live donors for Strongyloides, T. cruzi and WNV infection, specifically identifying at-risk populations and testing algorithms, including advantages, limitations and interpretation of results. In February 2013, the Organ Procurement and Transplantation Network mandated that transplant centers perform screening of living kidney donors prior to transplantation for Strongyloides, Trypanosoma cruzi and West Nile virus (WNV) infection if the donor is from an endemic area. However, specific guidelines for screening were not provided, such as the optimal testing modalities, timing of screening prior to donation and the appropriate selection of donors. In this regard, the American Society of Transplantation Infectious Diseases Community of Practice, together with disease-specific experts, has developed this viewpoint document to provide guidance for the testing of live donors for Strongyloides, T. cruzi and WNV infection, specifically identifying at-risk populations and testing algorithms, including advantages, limitations and interpretation of results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".