Transfusion-related mortality: the ongoing risks of allogeneic blood transfusion and the available strategies for their prevention
Bibliographic record
Abstract
As the risks of allogeneic blood transfusion (ABT)-transmitted viruses were reduced to exceedingly low levels in the US, transfusion-related acute lung injury (TRALI), hemolytic transfusion reactions (HTRs), and transfusion-associated sepsis (TAS) emerged as the leading causes of ABT-related deaths. Since 2004, preventive measures for TRALI and TAS have been implemented, but their implementation remains incomplete. Infectious causes of ABT-related deaths currently account for less than 15% of all transfusion-related mortality, but the possibility remains that a new transfusion-transmitted agent causing a fatal infectious disease may emerge in the future. Aside from these established complications of ABT, randomized controlled trials comparing recipients of non-white blood cell (WBC)-reduced versus WBC-reduced blood components in cardiac surgery have documented increased mortality in association with the use of non-WBC-reduced ABT. ABT-related mortality can thus be further reduced by universally applying the policies of avoiding prospective donors alloimmunized to WBC antigens from donating plasma products, adopting strategies to prevent HTRs, WBC-reducing components transfused to patients undergoing cardiac surgery, reducing exposure to allogeneic donors through conservative transfusion guidelines and avoidance of product pooling, and implementing pathogen-reduction technologies to address the residual risk of TAS as well as the potential risk of the next transfusion-transmitted agent to emerge in the foreseeable future.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".