Nucleic Acid Testing (NAT) of Organ Donors: Is the ‘Best’ Test the Right Test? A Consensus Conference Report
Bibliographic record
Abstract
Nucleic acid testing (NAT) for HIV, HBV and HCV shortens the time between infection and detection by available testing. A group of experts was selected to develop recommendations for the use of NAT in the HIV/HBV/HCV screening of potential organ donors. The rapid turnaround times needed for donor testing and the risk of death while awaiting transplantation make organ donor screening different from screening blood-or tissue donors. In donors with no identified risk factors, there is insufficient evidence to recommend routine NAT, as the benefits of NAT may not outweigh the disadvantages of NAT especially when false-positive results can lead to loss of donor organs. For donors with identified behavioral risk factors, NAT should be considered to reduce the risk of transmission and increase organ utilization. Informed consent balancing the risks of donor-derived infection against the risk of remaining on the waiting list should be obtained at the time of candidate listing and again at the time of organ offer. In conclusion, there is insufficient evidence to recommend universal prospective screening of organ donors for HIV, HCV and HBV using current NAT platforms. Further study of viral screening modalities may reduce disease transmission risk without excessive donor loss.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".