Human T‐cell lymphotropic virus: A simulation model to estimate residual risk with universal leucoreduction and testing strategies in Canada
Bibliographic record
Abstract
Background and Objectives In Canada, transfusion transmission risk of Human T‐cell lymphotropic virus ‐I/II (HTLV) is addressed by universal leucoreduction and universal antibody testing. We aimed to estimate the risk with the current policy, if testing only first‐time donors and if testing were stopped. Materials and Methods Monte Carlo simulation was employed to estimate the proportion of red cell concentrate, random donor platelet and apheresis platelet units that would be released into inventory in each scenario (10 billion donors each). The model estimated the number of HTLV‐positive donations not intercepted by testing, randomly assigned the number of HTLV particles/100 leucocytes using proportions from published data and randomly selected a postleucoreduction leucocyte count from quality control data. Units were considered infectious if ≥9 × 104 copies of HTLV provirus. Results With universal leucoreduction in place, the residual risk of releasing an HTLV potentially infectious unit with universal testing was 1 in 1·2 billion units (0, 1 in 55·9 million), with testing only first‐time donors 1 in 7·1 million (0, 1 in 1·05 million) and with no testing 1 in 1·0 million (0, 1 in 178 600). The efficacy of leucoreduction was >99·5% (lower bound 95·7%) for all scenarios. Conclusion With universal leucoreduction in place, switching from universal testing to testing first‐time donors would incur very low risk.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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".