Bibliographic record
Abstract
Over the past decades, the incidence of clinically significant transfusion-transmitted diseases has been dramatically reduced. These reductions have occurred because of a multifocal approach to the collection, processing, and release of blood and blood components. Research has focused on characterizing specific pathogens and their infectious patterns, especially the early viremic phase. Donor motivation and characteristics have been investigated, and restrictive eligibility criteria have been established. Regulatory oversight has been strengthened in the United States and hemovigilance systems established in many countries of the European Union, Canada, and Japan to identify new and emerging infectious and noninfectious transfusion risks. Such systems are required because many emerging pathogens will elude the stringent and sensitive donor testing already in place which, unfortunately, requires advanced technologies. Despite the remarkable progress, the processes of delivering a transfusion to a patient provide additional opportunity for risk, but have been less- well studied and the solutions have been less-well defined. Specific areas of concern include the collection and correct identification of patient samples, selection of modified or unique products, blood administration process, and establishment of specific indications for transfusion. In this chapter, we review the status of transfusion safety in the United States and discuss some emerging infectious and noninfectious risks of transfusion that will become future areas for basic and translational research. The disparities that exist in blood safety in developing countries' red cell alloimmunization and autoimmunity will not be covered.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".