Platelet transfusion associated risks and improvement of platelet transfusion safety with Amotosalen/UVA pathogen inactivation technology
Bibliographic record
Abstract
Despite the introduction of multiple measures to minimize the risk of transfusion-transmitted infections, there is still a residual risk of platelet transfusions, especially but not only for bacterial sepsis. Recently, the importance of a reliable hemovigilance system has been underlined and an up to 10-fold difference in the reporting of bacterial transmissions between active and passive reporting has been demonstrated. Another reason for a misjudging of the blood safety may derive from the fact that some pathogens cause obvious damage to critically ill and/or immunocompromised patients only and asymptomatic infections in immuno-competent recipients and thus are not being reported. Pathogen inactivation for platelets, a proactive approach not only broadly inactivating pathogens, but also white blood cells, could minimize the risk of transfusion transmitted infections and graft versus host disease due to residual leukocytes. Only the INTERCEPT technology has received approvals from the regulatory agencies in France, Germany and Switzerland, United States and Canada. That technology utilizes photoactive methods to modify nucleic acids. Long-term routine clinical experience, also with children and neonates, shows the safety and efficacy of INTERCEPT platelet transfusions. The national hemovigilance data of Switzerland, France and Belgium as well as single-center routine use studies show an improved clinical outcome in the acute as well as in the prophylactic setting with a significant decrease in septic and other non-hemolytic transfusion reactions, as well as prevention of graft-versus-host disease.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".