Anatomy-Based Eligibility Measure for Robotic-Assisted Bypass Surgery
Bibliographic record
Abstract
OBJECTIVE: Robotic-assisted endoscopic single-vessel small thoracotomy allows clinicians to perform coronary artery bypass grafting surgery in a minimally invasive manner using the da Vinci Surgical System. Not all patients are suitable for this technique, and the lack of an appropriate method for patient eligibility avoids completion of the procedure robotically. The objective of this study was to develop a patient eligibility method based on the anatomy of the chest of the patient. METHODS: Preoperative computed tomography thorax scans of 110 patients were analyzed. Two-dimensional measurements taken on the axial images were used with the goal of finding a relation between the anatomy of the patient and the completion of the procedure robotically. RESULTS: Patients with a distance from the left anterior descending coronary artery to the anterior chest wall of smaller than 15 mm have a 20% probability of requiring conversion of the procedure to open surgery. This probability increases if the chest of the patient is very elliptical, having an anterior-posterior dimension of less than 45% of the transverse dimension. CONCLUSIONS: The smaller the distance is from the left anterior descending artery to the anterior chest wall, the lower the chances are of completing the procedure robotically.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".