Coping with catastrophe: the value of endoscopic vascular injury training
Bibliographic record
Abstract
BACKGROUND: Inadvertent injury to the internal carotid artery (ICA), if poorly managed, not only presents a risk of exsanguination but can also result in significant long-term morbidity. Through the implementation of a novel animal model of endoscopic carotid artery injury (CAI), effective techniques to manage this scenario have been developed. The Vascular Injuries Workshop has trained over 110 surgeons in these techniques. This study reviews events of major arterial hemorrhage managed by surgeons who completed this vascular injury workshop training. METHODS: We report a retrospective multicenter case series of patients who required endoscopic management of intranasal major arterial hemorrhage. Delegates who had attended the course were contacted by e-mail and surveyed with regard to instances of major arterial bleeding and the management undertaken. Patient demographics, tumor type, factors influencing injury, management technique, and outcomes were reviewed. RESULTS: The cases reported herein are characterized as follows: 9 cases are reported in total, 3 male, 6 female; age range 37 to 82 years; 1 basilar artery, and 8 ICA injuries. Each case was successfully managed endoscopically with intraoperative muscle patch application. There were no deaths, 1 case of pseudoaneurysm with successful endovascular treatment, 2 cases of impaired carotid flow, and 1 carotid dissection was conservatively managed. There were no permanent neurological sequelae or other permanent morbidity. CONCLUSION: The Vascular Injuries Workshop arms surgeons with a structured approach to managing the surgical field and provides effective hemostatic techniques in the face of impending catastrophe. In comparison to the existing literature on ICA rupture, our results show trained surgeons are well equipped to achieve safe outcomes for their patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".