Multidisciplinary damage control management of life-threatening carotid blowout syndrome
Bibliographic record
Abstract
A 63-year-old patient arrived at our trauma center with profuse arterial bleeding from zone 2 on the right side of his neck. He was alert with a heart rate of 130 beats per minute and a blood pressure of 150/100 mm Hg. There was initial confusion regarding what had precipitated the bleeding. The patient clearly had undergone prior head and neck operation and radiation, marked by a chronic ulcerated wound with extensive fibrosis around the bleeding site and a laryngectomy stoma. The latter was quickly intubated and the patient resuscitated with blood products. Manual compression was applied to the bleeding site. We later learned that a scab had recently formed at the site and the patient had displaced it while shaving, eliciting the hemorrhage from a partially exposed right common carotid artery. Unbeknown to our team, the patient’s medical history was significant for squamous cell carcinoma of the larynx treated more than 15 years ago. Given the complexity of his cervical wound, the decision was made to proceed to the angiography suite, where an endovascular stent was successfully deployed to exclude the arterial defect and hemostasis. This allowed the wound to be fully visualized, revealing that the stent was exposed within the artery (figure 1). Figure 1 Exposed stent in the right common carotid artery (arrow) within a chronic ulcerated fibrotic wound on the right side of the patient’s neck. An endotracheal tube inserted through the …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".