Active Clearance of Chest Tubes Reduces Re-Exploration for Bleeding After Ventricular Assist Device Implantation
Bibliographic record
Abstract
Chest tubes are utilized to evacuate shed blood after left ventricular assist device (LVAD) implantation, however, they can become clogged, leading to retained blood. We implemented a protocol for active tube clearance (ATC) of chest tubes to determine if this might reduce interventions for retained blood. A total of 252 patients underwent LVAD implantation. Seventy-seven patients had conventional chest tube drainage (group 1), whereas 175 patients had ATC (group 2). A univariate and multivariate analysis adjusting for the use of conventional sternotomy (CS) and minimally invasive left thoracotomy (MILT) was performed. Univariate analysis revealed a 65% reduction in re-exploration (43-15%, p < 0.001), and an 82% reduction in delayed sternal closure (DSC; 34-6%, p <0.001). In a sub-analysis of CS only, there continued to be statistically significant 53% reduction in re-exploration (45% vs. 21%, p = 0.0011), and a 77% reduction in DSC (35% vs. 8%, p < 0.001) in group 2. Using a logistic regression model adjusting for CS versus MILT, there was a significant reduction in re-exploration (odds ratio [OR] = 0.44 [confidence interval {CI} = 0.23-0.85], p = 0.014) and DSC (OR = 0.20 [CI = 0.08-0.46], p <0.001) in group 2. Actively maintaining chest tube patency after LVAD implantation significantly reduces re-exploration and DSC.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| 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 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".