Comparison of Endoscopic Robotic versus Sternotomy Approach for the Resection of Left Atrial Tumors
Bibliographic record
Abstract
OBJECTIVE: Primary cardiac tumors most commonly occur in the left atrium. The aim of this study was to compare outcomes among patients undergoing isolated left atrial tumor resection via sternotomy or robotic approach. METHODS: From 2003 to 2013, 69 patients underwent isolated left atrial tumor resection at 3 affiliated hospitals with either a sternotomy (n = 39) or robotic approach (n = 30). A retrospective review of prospectively collected data was performed, and outcomes were compared between the sternotomy and robotic groups. Univariate and multivariate analyses controlling for pertinent preoperative characteristics were performed. RESULTS: Patients' characteristics in the 2 groups were similar, with the exception of a history of chronic obstructive pulmonary disease (sternotomy, 12.8% vs robotic, 0%; P < 0.04) and elective surgical status (sternotomy, 64.1% vs robotic, 93.3%; P < 0.02). On univariate analysis, robotic-assisted surgery was associated with significantly shorter postoperative mechanical ventilation, intensive care unit (ICU) length of stay (LOS), hospital LOS, and a lower rate of perioperative blood transfusion. After controlling for patient comorbidity in a multiple logistic regression model, there remained a trend toward decreased blood transfusions (adjusted odds ratio, 0.33; CI, 0.09-1.20; P = 0.09), shorter ICU (16.3 fewer hours; P = 0.11), and hospital LOS (1.1 fewer days; P = 0.17) in the robotic group. There was one postoperative stroke in the sternotomy group and none in the robotic group (P = 0.21). CONCLUSIONS: Robotic-assisted left atrial tumor resection is feasible and may be associated with a lower incidence of perioperative blood transfusion as well as shorter ventilation time, and shorter ICU and hospital LOS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".