International review of blood donation nucleic acid amplification testing
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Nucleic acid amplification testing (NAT), in blood services context, is used for the detection of viral and parasite nucleic acids to reduce transfusion-transmitted infections. This project reviewed NAT for screening blood donations globally. MATERIALS AND METHODS: A survey on NAT usage, developed by the International Society of Blood Transfusion Working Party on Transfusion-transmitted Infectious Diseases (ISBT WP-TTID), was distributed through ISBT WP-TTID members. Data were analysed using descriptive statistics. RESULTS: Forty-three responses were received from 32 countries. Increased adoption of blood donation viral screening by NAT was observed over the past decade. NAT-positive donations were detected for all viruses tested in 2019 (proportion of donations positive by NAT were 0.0099% for human immunodeficiency virus [HIV], 0.0063% for hepatitis C virus [HCV], 0.0247% for hepatitis B virus [HBV], 0.0323% for hepatitis E virus [HEV], 0.0014% for West Nile virus [WNV] and 0.00005% for Zika virus [ZIKV]). Globally, over 3100 NAT-positive donations were identified as NAT yield or solely by NAT in 2019 and over 22,000 since the introduction of NAT, with HBV accounting for over half. NAT-positivity rate was higher in first-time donors for all viruses tested except WNV. During 2019, a small number of participants performed NAT for parasites (Trypanosoma cruzi, Babesia spp., Plasmodium spp.). CONCLUSION: This survey captures current use of blood donation NAT globally. There has been increased NAT usage over the last decade. It is clear that NAT contributes to improving blood transfusion safety globally; however, there is a need to overcome economic barriers for regions/countries not performing 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 | 0.005 | 0.015 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".