A new strategy for estimating risks of transfusion‐transmitted viral infections based on rates of detection of recently infected donors
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it