Preoperative Evaluation of Patient Anatomy to Increase Success of Robotics-Assisted Bypass Surgery
Bibliographic record
Abstract
OBJECTIVE: Robotics-assisted endoscopic atraumatic coronary artery bypass has been shown to be effective in reducing surgical morbidity and length of hospital stay. Unfortunately, the criteria for selecting eligible patients for this procedure are still primitive. This has motivated the use of preoperative computed tomography scans to establish patient eligibility. The objective of this study is to establish which image measurements can be correlated to procedure success. METHODS: A retrospective study was performed in 144 patients who underwent robotics-assisted coronary bypass surgery. After an initial set of 55 patients, preoperative computed tomography scans of the other patients were used to obtain patient specific measurements: the lateral distance between the midline of the sternum to the left anterior descending coronary artery and its depth from the skin surface, anteroposterior diameter of the thoracic cavity, and the transverse diameter of the thoracic cavity. The procedures were rated as successful if completed in a minimally invasive manner. Different combinations of the variables were evaluated and correlated with success. RESULTS: A strong correlation was found between success rate and the ratio of the lateral distance to the transverse diameter in the female patients only (0.532, P = 0.006). A ratio of less than 0.20 significantly increased the occurrence of conversion during this procedure in female cases. CONCLUSIONS: The lateral distance of the left anterior descending coronary artery from the midline divided by the transverse thoracic width of a female patient shows a significant correlation with procedure success. No significant correlations were found for male patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".