Relative efficacy of nucleic acid amplification testing and serologic screening in preventing hepatitis C virus transmission risk in seven international regions
Bibliographic record
Abstract
BACKGROUND: The relative contribution of serologic screening and nucleic acid testing (NAT) to prevent hepatitis C virus (HCV) transmission has not been rigorously addressed. STUDY DESIGN AND METHODS: Twenty-one blood organizations in seven geographical regions performing individual-donation (ID)-NAT in parallel with anti-HCV screening provided data from 10,897,105 donations to establish HCV infection rates in first-time, lapsed, and repeat donations. Screening efficacy was modeled for: anti-HCV alone, HCV antigen/antibody (combo), minipool (MP)-NAT in pools of 8 and 16 with anti-HCV, ID-NAT and anti-HCV, and ID-NAT alone. Probabilities of infectivity for red blood cell transfusions were estimated as 100% from window period (WP) and concordant HCV RNA/antibody-positive (concordantly positive [CP]) donations and 0.028% from anti-HCV-positive and RNA-negative probable resolved (PR) donations. RESULTS: There were 5146 confirmed infections (30 WP, 3827 CP, and 1289 PR). Infection rates and transmission risks varied substantially across regions and by donation status. Residual risk with ID-NAT and serology screening was estimated at one in 250,000 in Egypt and at one in 10,000,000 in other regions combined; risk would increase to one in 7300 and one in 312,000, respectively, if NAT had not been performed. ID-NAT with or without anti-HCV testing showed higher efficacy than either MP-NAT or combo assays, particularly in lapsed or repeat donors in whom 99.2, 98.5, and 93.2% of infectious donations were estimated to be interdicted by these respective testing strategies. CONCLUSIONS: The incremental efficacy of anti-HCV testing when ID- NAT screening is performed was minimal, particularly for screening lapsed and repeat donations.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".