Resuscitative Endovascular Balloon Occlusion of the Aorta
Bibliographic record
Abstract
The control of torso and junctional zone bleeding in combat casualties is particularly challenging because of its noncompressible nature. Resuscitative endovascular balloon occlusion of the aorta (REBOA) has demonstrated promise in translational large animal and early clinical series as an effective resuscitation and hemorrhage control adjunct. However, it is unknown what proportion of combat casualties has an injury pattern and clinical course that is amenable to REBOA deployment. The prospective UK Joint Theatre Trauma Registry was used to retrospectively identify all UK military personnel who has sustained a severe combat injury, defined as an Abbreviated Injury Scale of three or greater, in the course of 10 years. Patients were then divided into three groups based on Abbreviated Injury Scale injury pattern: no indications for REBOA, contraindications (mediastinal, cervical, and axillary hemorrhage), and indications (torso and pelvic hemorrhage). From a total of 1,317 patients, 925 (70.2%) had no indication, 148 (11.2%) had a contraindication, and 244 (18.5%) had an indication for REBOA. Within the group with indications for REBOA, there were 174 deaths: 79 at the point of wounding, 66 en route to hospital, and 29 in-hospital deaths. The median (interquartile range) time to death in patients dying en route was 75 (42-109) min, and the median prehospital time for casualties admitted to hospital was 61 (34-89) min. One-in-five severely injured UK combat casualties have a focus of hemorrhage in the abdomen or pelvic junctional region potentially amenable to REBOA deployment. The UK military should explore REBOA as a potential en route hemorrhage control and resuscitation adjunct.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".