Evaluating the Effect of Three-Dimensional Visualization on Force Application and Performance Time during Robotics-Assisted Mitral Valve Repair
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine the effect of three-dimensional (3D) binocular, stereoscopic, and two-dimensional (2D) monocular visualization on robotics-assisted mitral valve annuloplasty versus conventional techniques in an ex vivo animal model. In addition, we sought to determine whether these effects were consistent between novices and experts in robotics-assisted cardiac surgery. METHODS: A cardiac surgery test-bed was constructed to measure forces applied during mitral valve annuloplasty. Sutures were passed through the porcine mitral valve annulus by the participants with different levels of experience in robotics-assisted surgery and tied in place using both robotics-assisted and conventional surgery techniques. RESULTS: The mean time for both the experts and the novices using 3D visualization was significantly less than that required using 2D vision (P < 0.001). However, there was no significant difference in the maximum force applied by the novices to the mitral valve during suturing (P = 0.7) and suture tying (P = 0.6) using either 2D or 3D visualization. The mean time required and forces applied by both the experts and the novices were significantly less using the conventional surgical technique than when using the robotic system with either 2D or 3D vision (P < 0.001). CONCLUSIONS: Despite high-quality binocular images, both the experts and the novices applied significantly more force to the cardiac tissue during 3D robotics-assisted mitral valve annuloplasty than during conventional open mitral valve annuloplasty. This finding suggests that 3D visualization does not fully compensate for the absence of haptic feedback in robotics-assisted cardiac surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".