Biventricular pacing for end-stage heart failure: early experience in surgical vs. transvenous left ventricular lead placement
Bibliographic record
Abstract
Transvenous coronary sinus lead placement is currently the standard approach for left ventricular pacing. The aim of this study is to assess whether a mini-thoracotomy approach would be feasible and safe when used for cases in which transvenous procedures were ineffective or judged unlikely to succeed. Biventricular pacing was performed in 138 consecutive patients with 47 patients undergoing a mini-thoracotomy procedure. NYHA status, quality of life, electrical and echocardiographic data were assessed in the two groups over a follow-up period of 17.6+/-4.2 weeks. There was no significant difference in the preoperative characteristics in both groups other than a greater prevalence of renal failure and previous cardiac surgery among the surgical patients. The mean procedure time was significantly longer in the transvenous group. No significant differences were noted in the immediate or long-term pacing parameters. Two mortalities were observed in the surgical group >2 weeks following the procedure. During the follow-up period, we noted a comparable improvement in the echocardiographic parameters, QRS duration and NYHA status with both approaches. Our results suggest that even when performed on high-risk patients, epicardial lead placement through a mini-thoracotomy is beneficial and feasible as a 'rescue' procedure after a failed transvenous approach.
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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 | 0.003 |
| 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) | 0.001 | 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".