A DELPHI CONSENSUS ALGORITHM FOR MODERN REBOA PROGRAMS: EMPLOYING A TITRATABLE CATHETER AND PARTIAL AORTIC OCCLUSION TO ADVANCE THE PROCEDURE
Bibliographic record
Abstract
BACKGROUND: Technical advances in REBOA catheters have made partial aortic occlusion a more common hemorrhage mitigation strategy in trauma resuscitation. This technique balances hemorrhage control and distal ischemic concerns; however, there are limited data to guide best practices. We aim to provide a pragmatic guideline, based on expert consensus, on the use of pREBOA and partial aortic occlusion for modern REBOA programs. METHODS: A Delphi study was conducted based on 12 experts experienced in pREBOA and partial aortic occlusion. An initial questionnaire was distributed and results anonymously collated into consensus statements. These statements were then anonymously distributed and refined to reach 80% consensus. RESULTS: After the initial questionnaire and two rounds of polling, a total of 15 consensus statements were developed, all reaching >80% agreement. These statements focused around REBOA program development, early common femoral arterial access, REBOA placement, management, and occlusion/reperfusion strategies. CONCLUSION: This Delphi study provides guidance on how to leverage pREBOA and partial aortic occlusion as a resuscitative adjunct. It addresses thresholds for common femoral arterial access, triggers for occlusion, complete versus partial aortic occlusion, computed tomography imaging, pREBOA with thoracic injuries, proximal and distal blood pressure goals, updated ischemia times, strategies for reperfusion, and sheath management. This algorithm provides a framework for the development of REBOA programs that encompasses new REBOA technologies with partial aortic occlusion and guides the user from patient presentation to sheath removal in a modern era.
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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 | 0.250 | 0.197 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".