Risks associated with red blood cell transfusions: potential benefits from application of pathogen inactivation
Bibliographic record
Abstract
BACKGROUND: Red blood cell (RBC) transfusion risks could be reduced if a robust technology for pathogen inactivation of RBC (PI-RBCs) were to be approved. MATERIALS AND METHODS: Estimates of per-unit and per-patient aggregate infectious risks for conventional RBCs were calculated; the latter used patient diagnosis as a determinant of estimated lifetime exposure to RBC units. Existing in vitro data for the two technologies under development for producing PI-RBCs and the status of current clinical trials are reviewed. RESULTS: Minimum and maximum per-unit risk were calculated as 0.0003% (1 in 323,000) and 0.12% (1 in 831), respectively. The minimum estimate is for known lower-risk pathogens while the maximal estimate also includes an emerging infectious agent (EIA) and endemic area Babesia risk. Minimum and maximum per-patient lifetime risks by diagnosis grouping were estimated as 1.5 and 3.3%, respectively, for stem cell transplantation (which includes additional risk for cytomegalovirus transmission); 1.2 and 3.7%, respectively, for myelodysplastic syndrome; and 0.2 and 44%, respectively, for hemoglobinopathy. DISCUSSION: There is potential for PI technologies to reduce infectious RBC risk and to provide additional benefits (e.g., prevention of transfusion-associated graft-versus-host disease and possible reduction of alloimmunization) due to white blood cell inactivation. PI-RBCs should be viewed in the context of having a fully PI-treated blood supply, enabling a blood safety paradigm shift from reactive to proactive. Providing insurance against new EIAs. Further, when approved, the use of PI for all components may catalyze operational changes in blood donor screening, laboratory testing, and component manufacturing.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".