A patient‐oriented risk–benefit analysis of pathogen‐inactivated blood components: application to apheresis platelets in the United States
Bibliographic record
Abstract
We performed a risk-benefit analysis for implementation of pathogen-inactivated (PI) apheresis platelets (APs) in the United States, focusing on the amotosalen/ultraviolet-A system. Risks and benefits were quantified per patient assuming a mean of 6 AP units per treatment cycle and using available clinical data, mathematical modeling, and observational studies. Current risks associated with AP transfusion can be divided into known partially addressed risks, known well-addressed risks, and unknown risks associated with acute or chronic emerging infectious agents (EIAs). Bacterial contamination dominates the first category, at a per-patient rate of 1:250, which correlates with an estimated septic transfusion reaction rate of 1:1000. Quantitation of per-patient EIA risk was modeled to be between 1:370 (acute) and 1:667 (chronic). Due to its broad range of action PI is expected to reduce or eliminate these infectious risks and also to reduce the rate of febrile transfusion reactions and possibly alloimmunization. These benefits are weighed against 1) concerns for excess bleeding, 2) an apparent increase in acute respiratory distress syndrome in the initial report of the SPRINT clinical trial, and 3) the possible toxicity associated with the introduction of a new chemical into platelet (PLT) units. However, transfusion of an estimated 100,000 patients with PI PLTs worldwide has occurred without reported serious adverse effects. We conclude that evidence indicates a favorable risk-benefit profile for the implementation of PLT PI and argues for a path forward toward US regulatory approval.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".