An international review of the characteristics of viral nucleic acid‐amplification testing (NAT) reveals a trend towards the use of smaller pool sizes and individual donation NAT
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Nucleic acid-amplification testing (NAT) is used for screening blood donations/donors for blood-borne viruses. We reviewed global viral NAT characteristics and NAT-yield confirmatory testing used by blood operators. MATERIALS AND METHODS: NAT characteristics and NAT-yield confirmatory testing used during 2019 was surveyed internationally by the International Society of Blood Transfusion Working Party Transfusion-Transmitted Infectious Diseases. Reported characteristics are presented herein. RESULTS: NAT was mainly performed under government mandate. Human immunodeficiency virus (HIV), hepatitis C virus (HCV) and hepatitis B virus (HBV) NAT was performed on all donors and donation types, while selective testing was reported for West Nile virus, hepatitis E virus (HEV), and Zika virus. Individual donation NAT was used for HIV, HCV and HBV by ~50% of responders, while HEV was screened in mini-pools by 83% of responders performing HEV NAT. Confirmatory testing for NAT-yield samples was generally performed by NAT on a sample from the same donation or by NAT and serology on samples from the same donation and a follow-up sample. CONCLUSION: In the last decade, there has been a trend towards use of smaller pool sizes or individual donation NAT. We captured characteristics of NAT internationally in 2019 and provide insights into confirmatory testing approaches used for NAT-yields, potentially benefitting blood operators seeking to implement NAT.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".