Implementing pathogen reduction technology while discontinuing blood donor deferral criteria for sexual risk behaviors: A simulation study
Bibliographic record
Abstract
BACKGROUND: Combining pathogen reduction technology (PRT) with blood screening may alleviate concerns over the risk of transfusion-transmitted infections (TTI) and support changes in blood donor selection to potentially increase blood availability. This study aimed to estimate the residual risk of human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV) transfusion-transmission in Canada after implementing PRT, while eliminating deferrals for sexual risk behaviors. STUDY DESIGN AND METHODS: A probabilistic approach that combined Bayesian networks with Monte Carlo simulations was used to estimate the risk of transfusing HIV-, HBV-, or HCV-contaminated blood components. Different scenarios were considered to compare the current residual risk after PRT implementation, with and without donor deferral criteria for sexual risk behaviors. Donor profiles and blood component outcomes were simulated based on a literature review including the prevalence and incidence of HIV, HBV, and HCV in the Canadian blood donor population; the use of current blood screening assays; and HIV, HBV, and HCV blood donor viral loads. RESULTS: In the universal PRT scenario (i.e., with PRT/without deferral criteria), the estimated risks of HIV, HBV, and HCV transmission were significantly lower than those in the currently observed scenario (i.e., without PRT/with deferral criteria). CONCLUSIONS: This risk model suggests that PRT for platelets and plasma (and eventually for RBCs when available) significantly reduces the residual risks of HIV, HBV and HCV transfusion-transmission and could enable the removal of blood donor deferral criteria for sexual risk behaviors.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".