Off-Pump Atrial Septal Defect Closure Using the Universal Cardiac Introducer <sup>®</sup>
Bibliographic record
Abstract
OBJECTIVE: : Optimal atrial septal defect (ASD) closure should combine off-pump techniques with the effectiveness and versatility of open-heart techniques. We report our experience with off-pump ASD closure using the Universal Cardiac Introducer (UCI) in a porcine model. The goal was to create an ASD over the fossa ovale (FO) and position a patch over the ASD under ultrasound (US) imaging and augmented virtual reality guidance. METHODS: : An US probe (tracked with a magnetic tracking system) was positioned into the esophagus (transesophageal echocardiographic probe) for real-time image-guidance. The right atrium (RA) of six pigs was exposed via a right lateral thoracotomy or medial sternotomy. The UCI was attached to the RA wall. A punching tool was introduced via the UCI, navigated and positioned, under US guidance, to create an ASD into the FO. A patch with its holder and a stapling device were introduced into the RA via the UCI. The patch was positioned on the ASD. Occlusion of the ASD was determined using US and Doppler imaging. RESULTS: : The FO membrane was excised successfully in all animals. US image-guidance provided excellent visualization. The patch was positioned in all cases with complete occlusion of the ASD. The stapling device proved too bulky, impeding circumferential positioning. CONCLUSIONS: : Using the UCI, ASD closure was safe and feasible. US imaging, combined with virtual and augmented reality provided accurate navigating and positioning. This study also provided valuable information on the future design of anchoring devices for intracardiac procedures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".