A new strategy for estimating risks of transfusion‐transmitted viral infections based on rates of detection of recently infected donors
Bibliographic record
Abstract
BACKGROUND: Estimates for human immunodeficiency virus (HIV)-1 and hepatitis C virus (HCV) transfusion-transmitted risks have relied on incidence derived from repeat donor histories and imprecise estimates for infectious, preseroconversion window periods (WPs). STUDY DESIGN AND METHODS: By use of novel approaches, WPs were estimated by back-extrapolation of acute viral replication dynamics. Incidence was derived from the yield of viremic, antibody-negative donations detected by routine minipool nucleic acid testing (MP-NAT) of 37 million US donations (1999-2002) or from sensitive/less-sensitive HIV-1 enzyme immunoassay (S/LS-EIA) results for seropositive samples from 6.5 million donations (1999). Incidences and WPs were combined to calculate risks and project yield of individual donation (ID)-NAT. RESULTS: The HIV-1 WP from presumed infectivity (1 copy/20 mL) to ID-NAT detection was estimated at 5.6 days, and the periods from ID to MP-NAT detection and from MP-NAT to p24 detection at 3.4 and 6.0 days, respectively; corresponding estimates for HCV were 4.9, 2.5, and 50.9 days (the latter represents period from MP-NAT to HCV antibody detection). The HIV-1 incidence projected from MP-NAT yield or from S/LS-EIA data was 1.8 per 100,000 person-years, resulting in a corresponding HIV-1 transfusion-transmitted risk of 1 in 2.3 million. The HCV incidence from MP-NAT yield was 2.70 per 100,000 person-years with a corresponding risk of 1 in 1.8 million donations. Conversion from MP-NAT to ID-NAT was projected to detect two to three additional HIV-1 and HCV infectious units annually. CONCLUSIONS: MP-NAT yield and S/LS-EIA rates can accurately project transfusion risks. HCV and HIV-1 risks, currently estimated at 1 per 2 million units, could be reduced to 1 in 3 to 4 million units by ID-NAT screening.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".