Systematic review to evaluate algorithms for REBOA use in trauma and identify a consensus for patient selection
Bibliographic record
Abstract
Background: Patient selection for resuscitative endovascular balloon occlusion of the aorta (REBOA) has evolved during the last decade. A recent multicenter collaboration to implement the newest generation REBOA balloon catheter identified variability in patient selection criteria. The aims of this systematic review were to compare recent REBOA patient selection guidelines and to identify current areas of consensus and variability. Methods: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review of clinical practice guidelines for REBOA patient selection in trauma. Published algorithms from 2015 to 2022 and institutional guidelines from a seven-center REBOA collaboration were compiled and synthesized. Results: Ten published algorithms and seven institutional guidelines on REBOA patient selection were included. Broad consensus exists on REBOA deployment for blunt and penetrating trauma patients with non-compressible torso hemorrhage refractory to blood product resuscitation. Algorithms diverge on precise systolic blood pressure triggers for early common femoral artery access and REBOA deployment, as well as the use of REBOA for traumatic arrest and chest or extremity hemorrhage control. Conclusion: Although our convenience sample of institutional guidelines likely underestimates patient selection variability, broad consensus exists in the published literature regarding REBOA deployment for blunt and penetrating trauma patients with hypotension not responsive to resuscitation. Several areas of patient selection variability reflect individual practice environments. Level of evidence: Level 5, systematic review.
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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 | 0.079 | 0.318 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".