Superior vena cava tear during transvenous lead extraction: Medical management in hemodynamically stable patients
Bibliographic record
Abstract
INTRODUCTION: Superior vena cava (SVC) tear is the most lethal complication during transvenous lead extraction (TLE) with a mortality rate as high as 50%. Treatment involves aggressive attempts to maintain cardiac output and immediate sternotomy to localize and repair the vascular tear. Occlusion balloons have been developed to provisionally occlude the lacerated SVC and to provide hemodynamic stability allowing time for surgery. In case of mediastinal hematoma without hemodynamic instability, the strategy remains unclear. METHODS AND RESULTS: We describe two cases of SVC tear during TLE. The first case was a 60-year-old man who presented with a right ventricular single-chamber defibrillator lead fracture and innominate vein stenosis. The RV lead was removed using a laser sheath causing a mediastinal hematoma with no active bleeding during surgical exploration few hours later. The second case was a 28-year-old man that presented with a right atrial (RA) lead fracture and RV lead insulation failure in a dual-chamber defibrillator (ICD). CONCLUSION: Both the RA and RV leads were removed with mechanical sheaths, and a mediastinal hematoma was medically managed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".