A minimally invasive technique for closing an iatrogenic subclavian artery cannulation using the Angio-Seal closure device: two case reports
Bibliographic record
Abstract
INTRODUCTION: In the two cases described here, the subclavian artery was inadvertently cannulated during unsuccessful access to the internal jugular vein. The puncture was successfully closed using a closure device based on a collagen plug (Angio-Seal, St Jude Medical, St Paul, MN, USA). This technique is relatively simple and inexpensive. It can provide clinicians, such as intensive care physicians and anesthesiologists, with a safe and straightforward alternative to major surgery and can be a life-saving procedure. CASE PRESENTATION: In the first case, an anesthetist attempted ultrasound-guided access to the right internal jugular vein during the preoperative preparation of a 66-year-old Caucasian man. A 7-French (Fr) triple-lumen catheter was inadvertently placed into his arterial system. In the second case, an emergency physician inadvertently placed a 7-Fr catheter into the subclavian artery of a 77-year-old Caucasian woman whilst attempting access to her right internal jugular vein. Both arterial punctures were successfully closed by means of a percutaneous closure device (Angio-Seal). No complications were observed. CONCLUSIONS: Inadvertent subclavian arterial puncture can be successfully managed with no adverse clinical sequelae by using a percutaneous vascular closure device. This minimally invasive technique may be an option for patients with non-compressible arterial punctures. This report demonstrates two practical points that may help clinicians in decision-making during daily practice. First, it provides a practical solution to a well-known vascular complication. Second, it emphasizes a role for proper vascular ultrasound training for the non-radiologist.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".