Preoperative planning of robotics-assisted minimally invasive coronary artery bypass grafting
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Bibliographic record
Abstract
This paper outlines a framework for the preoperative planning of robotics-assisted minimally invasive cardiac surgery with application to coronary artery bypass grafting. The intent of the proposed framework is to improve surgical outcomes by considering the intraoperative requirements of the robotic manipulators and the anatomical geometry of the patient's chest. This includes target reachability, instrument dexterity for critical surgical tasks and collision avoidance. Given the patient's preoperative chest computed tomography images, the planning framework aims to determine the optimal location of the access ports on the ribcage, along with the optimal pose of the robotic arms relative to the patient's anatomy. The proposed multi-objective optimality criteria consist of a measure of clearance as well as a new collective kinematic measure. The minimum distances among the robot arms provides a measure for the likelihood of collisions. The proposed kinematic measure is composed of two modified manipulability indices that are dimensionally homogeneous and, in contrast to previously-used measures, are more likely to yield isotropic force and torque distributions when optimized for surgical interventions. The results of a case study illustrate the compatibility of the framework with general guidelines used by experienced surgeons for port selection. Furthermore, the framework surpasses those guidelines by ensuring the feasibility of the solutions in the sense of collision avoidance and surgical target reachability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it