A framework for preoperative planning of robotics-assisted minimally invasive cardiac surgery (RAMICS) under geometric uncertainty
Bibliographic record
Abstract
In this paper, robust preoperative planning of RAMICS is formulated. The intent of the proposed planning framework is to improve surgical outcomes by contemplating the intraoperative conditions of the surgical procedure and the geometry of the patient's thoracic anatomy. This includes improvements in target reachability, instrument dexterity for critical surgical tasks, surgical task feasibility and visibility. Given the patient's preoperative computed tomography images of the chest, 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. To minimize susceptibility of the results to intraoperative geometric uncertainty, the planning is formulated as a Generalized Semi-Infinite Program (GSIP) with a convex lower level problem and a multi-criteria objective function. By solving the GSIP, tolerable geometric uncertainty within the task space is increased by eliminating the likelihood of collisions and joint limit violation in a neighborhood of the surgical target.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".