Development of a national out-of-hospital transfusion protocol: a modified RAND Delphi study
Bibliographic record
Abstract
BACKGROUND: Early resuscitation with blood components or products is emerging as best practice in selected patients with trauma and medical patients; as a result, out-of-hospital transfusion (OHT) programs are being developed based on limited and often conflicting evidence. This study aimed to provide guidance to Canadian critical care transport organizations on the development of OHT protocols. METHODS: The study period was July 2021 to June 2022. We used a modified RAND Delphi process to achieve consensus on statements created by the study team guiding various aspects of OHT in the context of critical care transport. Purposive sampling ensured representative distribution of participants in regard to geography and relevant clinical specialties. We conducted 2 written survey Delphi rounds, followed by a virtual panel discussion (round 3). Consensus was defined as a median score of at least 6 on a Likert scale ranging from 1 ("Definitely should not include") to 7 ("Definitely should include"). Statements that did not achieve consensus in the first 2 rounds were discussed and voted on during the panel discussion. RESULTS: Seventeen subject experts participated in the study, all of whom completed the 3 Delphi rounds. After the study process was completed, a total of 39 statements were agreed on, covering the following domains: general oversight and clinical governance, storage and transport of blood components and products, initiation of OHT, types of blood components and products, delivery and monitoring of OHT, indications for and use of hemostatic adjuncts, and resuscitation targets of OHT. INTERPRETATION: This expert consensus document provides guidance on OHT best practices. The consensus statements should support efficient and safe OHT in national and international critical care transport programs.
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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 | 0.290 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".