Current state of technical transfusion medicine practice for out‐of‐hospital blood transfusion in Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Canadian out-of-hospital blood transfusion programmes (OHBTPs) are emerging, to improve outcomes of trauma patients by providing pre-hospital transfusion from the scene of injury, given prolonged transport times. Literature is lacking to guide its implementation. Thus, we sought to gather technical transfusion medicine (TM)-specific practices across Canadian OHBTPs. MATERIALS AND METHODS: A survey was sent to TM representatives of Canadian OHBTPs from November 2021 to March 2022. Data regarding transport, packaging, blood components and inventory management were included and reported descriptively. Only practices involving Blood on Board programme components for emergency use were included. RESULTS: OHBTPs focus on helicopter emergency medical service programmes, with some supplying fixed-wing aircraft and ground ambulances. All provide 1-3 coolers with 2 units of O RhD/Kell-negative red blood cells (RBCs) per cooler, with British Columbia trialling coolers with 2 units of pre-thawed group A plasma. Inventory exchanges are scheduled and blood components are returned to TM inventory using visual inspection and internal temperature data logger readings. Coolers are validated to storage durations ranging from 72 to 124 h. All programmes audit to manage wastage, though there is no consensus on appropriate benchmarks. All programmes have a process for documenting units issued, reconciliation after transfusion and for transfusion reaction reporting; however, training programmes vary. Common considerations included storage during extreme temperature environments, O-negative RBC stewardship, recipient notification, traceability, clinical practice guidelines co-reviewed by TM and a common audit framework. CONCLUSION: OHBTPs have many similarities throughout Canada, where harmonization may assist in further developing standards, leveraging best practice and national coordination.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".