Eligibility for Minimally Invasive Coronary Artery Bypass
Bibliographic record
Abstract
OBJECTIVE: A variable that necessitates conversion to a conventional full-sternotomy coronary artery bypass procedure from a robotic-assisted endoscopic single-vessel small thoracotomy is the inability to visualize the left anterior descending coronary artery within the surrounding epicardial adipose tissue using the endoscopic camera. The purpose of this study was to determine whether anatomical properties of the epicardial adipose tissue examined using preoperative computed tomography (CT) images are able to predict and thus reduce the need for intraoperative conversion based on effective preoperative exclusion criteria. METHODS: Retrospective analysis of patient preoperative CT angiography scans from both converted (n = 17) and successful robotic-assisted (n = 17) procedures was performed. Where possible, measurements of epicardial adipose tissue were acquired from axial slices, at the most accessible segment of the left anterior descending coronary artery. RESULTS: Results indicate that patients who successfully underwent the endoscopic single-vessel small thoracotomy procedure (mean ± SD depth, 4.9 ± 1.9 mm) had significantly less epicardial adipose tissue (38%, P = 0.002) overlying the vessel toward the lateral chest wall than those who were converted to the full-sternotomy approach intraoperatively (mean ± SD depth, 7.9 ± 3.2 mm). Using this as a retrospective exclusion criterion reduces the conversion rate for this group by 47%, while maintaining a high specificity (94%). No significant differences exist between the two groups with respect to the remaining epicardial adipose tissue measurements or body mass index. CONCLUSIONS: The addition of CT angiography measurements of the epicardial adipose tissue overlying the left anterior descending coronary artery may enhance preoperative surgical planning for this procedure, thereby reducing the instances of procedural changes.
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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 | 0.000 | 0.001 |
| 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) | 0.008 | 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".