Prevalence of human immunodeficiency virus <scp>RNA</scp> and antibody in first‐time, lapsed, and repeat blood donations across five international regions and relative efficacy of alternative screening scenarios
Bibliographic record
Abstract
BACKGROUND: Twenty-one blood organizations from five geographical regions provided HIV individual donation (ID)-NAT and serology data on 11,787,610 donations. Infections were classified as anti-HIV-/RNA+ window period (WP), anti-HIV+/RNA+ concordant positive (CP) or anti-HIV+/RNA- elite controller (EC). Residual risk and efficacy of several screening scenarios were estimated for first time, lapsed and repeat donations. METHODS: WP residual risk estimates assumed a 50% infectious dose of 3.16 virions and a 50% detection limit of 2.7 HIV RNA copies/mL for ID-NAT and 10,000 copies/mL for p24Ag. Infectivity for CP (100%) and EC (2.2%) donations was estimated based on viral load distributions and 100-fold reduced infectivity by antibody neutralization as reported elsewhere. Efficacy was calculated as proportion of transmission risk removed from baseline (i.e. in absence of any screening). RESULTS: There was no significant difference in transmission risk between lapsed and repeat donations in any region. Risk was 3.8-fold higher in first time than combined lapsed/repeat donations in South Africa but not in other regions. Screening strategies were most efficacious at interdicting infectious transfusions in first time (98.7-99.8%) followed by lapsed (97.6-99.7%) and repeat (86.8-97.7%) donations in all regions combined. In each donor category the efficacy of ID-NAT alone (97.7-99.8%) was superior to that of minipool (MP)-NAT/anti-HIV (95.0-99.6%) and p24 Ag/anti-HIV (89.8-99.1%). CONCLUSIONS: Efficacy patterns were similar by donor/donation status in each region despite large differences in HIV prevalence and transmission risk. As similar data become available for HBV and HCV, this modeling may be useful in cost effectiveness analyses of alternative testing scenarios.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".