Mitral valve implantation using off-pump closed beating intracardiac surgery: a feasibility study
Bibliographic record
Abstract
We have developed the Universal Cardiac Introducer (UCI) with the aim of modernizing the off-pump, closed, beating, intracardiac approach. This paper reports our ongoing experience with positioning of a prosthetic MV, under image-guidance, substituting for direct vision. The UCI is comprised of two detachable parts: an attachment-cuff and an airlock-introductory chamber for bulky tools. A prosthetic MV was introduced into the left atrium in 12 pigs via the UCI (LA appendage). Transesophageal and 4D epicardial ultrasound were used for guidance. Limitations of ultrasound imaging prompted the development of a multimodality virtual reality (VR) system introduced in the last three animals. There were no complications associated with cardiac access, while achieving proper valve positioning. TEE contributed to navigating, while 4D epicardial ultrasound was adequate for positioning the prosthesis into the MV orifice. VR provided a 3D context for real-time US imaging with precise navigation and positioning using augmented reality representation of the valve. We demonstrated the feasibility of positioning MV prostheses via the UCI. These results suggest the tremendous potential of virtual reality in making access safe and effective for many intracardiac targets, with the ultimate goal of a safe, versatile, clinical application.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".