Platelet component safety in the era of new advancements in bacterial screening and pathogen reduction: A congress report of the 2024 <scp>ISBT</scp> Transfusion‐Transmitted Infectious Diseases Working Party, Bacteria Subgroup
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: High-income countries have successfully enhanced platelet component (PC) safety with the implementation of mitigation strategies including donor screening, skin disinfection, first aliquot diversion and PC bacterial screening or treatment with pathogen reduction technologies (PRT). This review discusses the experiences of several institutions with the adoption of bacterial screening methods and/or PRT and highlights residual safety risks. MATERIALS AND METHODS: Data from the American Red Cross (ARC), Australian Red Cross Lifeblood (Lifeblood), Canadian Blood Services (CBS), the Établissement Français du Sang (EFS) and National Health Service Blood and Transplant (NHSBT) were presented at the International Society of Blood Transfusion (ISBT) Congress, Transfusion-Transmitted Infectious Diseases meeting (Barcelona, June 2024) and were summarised in this report. RESULTS: PC screening with the automated BACT/ALERT culture system began in 2004 in the ARC and CBS, while the system was adopted in 2008 and 2011 by Lifeblood and NHSBT, respectively. Implementation of PC treatment with the PRT INTERCEPT started in 2016, 2022, and 2006 in the ARC, CBS, and EFS, correspondingly. CONCLUSIONS: PC screening and PC treatment with PRT have significantly increased product safety. However, there are still residual safety risks posed by challenging organisms such as sporulated Bacillus spp. and toxin-producing Staphylococcus aureus.
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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 | 0.019 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".