Efficacy of Different Testing Scenarios in Reducing Transfusion-Transmitted Hepatitis B Virus (TT-HBV) Infection Risk
Bibliographic record
Abstract
The efficacy of different screening scenarios in reducing hepatitis B virus (HBV) transmission risk as compared to the risk without screening was modeled in 9,337,110 donations from four geographical regions that had been subjected to hepatitis B surface antigen (HBsAg) and individual donation nucleic acid amplification testing (ID-NAT). We used the Weusten models for estimating infectivity risk for Red Blood Cell (RBC) transfusions in eight HBV infection stages and then evaluated multiple screening strategies based on minipool (MP) and ID-NAT options of different sensitivity for their efficacy in reducing this risk. The efficacy in reducing HBV transmission risk by screening scenarios across the regions varied between 81% (HBsAg only) and 99.2% (ID-NAT and anti-HBc). Highly sensitive ID-NAT alone achieved a slightly higher risk reduction (97.6−99.0%) than minipool of 6 donations (MP6)-NAT in combination with HBsAg and anti-HBc (96.3−98.7%). In ID-NAT screened lapsed and repeat donors, the additional risk removed by HBsAg testing was minimal (<0.1%). The modeling outcomes in this and two previous reports using this multi-regional database suggest that one could consider an ID-NAT alone testing scenario as an alternative to MP-NAT and serology-based testing algorithms and restrict serologic testing to first-time donors only.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".