How we implement a prehospital transfusion program
Bibliographic record
Abstract
BACKGROUND: Prehospital blood transfusions are necessary in certain situations that involve significant bleeding to prevent fatality. STUDY DESIGN AND METHODS: Members of the AABB Donor and Blood Component Management Prehospital working group collaborated to provide a consensus review of best practices in the implementation of a prehospital transfusion program. RESULTS: Several logistic paradigms exist in terms of how blood products are provided to emergency medical services (EMS). Each paradigm has pros and cons and should be adopted according to the specific environmental and operational needs of the states, regions, and agencies they serve. Low titer group O whole blood (LTOWB) has been successfully used in prehospital programs, but blood components such as packed red blood cells (pRBCs) and liquid plasma may also be used. All blood products carried by EMS must be transported and stored according to the same regulatory requirements set by the United States Food and Drug Administration. Processes must be in place to minimize wastage of blood products carried by EMS due to product expiration to preserve this limited resource. Barriers to the implementation of a prehospital transfusion program include scope of practice limitations, program costs, blood product reimbursement, cost of wastage, and collaboration between prehospital agencies, blood suppliers, and hospital transfusion services. DISCUSSION: Program implementation requires significant collaboration among different business entities with a carefully written and executed agreement. Despite the barriers, prehospital transfusion programs are worthy endeavors with the potential to save lives.
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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 | 0.064 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".