Left Atrial Vein Pacing:
Bibliographic record
Abstract
Biatrial pacing is a promising new therapy for drug refractory AF. This article reports two studies. First, an initial 14-patient experience with a novel technique for biatrial pacing. The authors attempted to pace from the LA vein branches of the proximal CS for LA stimulation. LA vein pacing would potentially offer the advantages of greater interatrial synchronization and possibly greater reduction in AF burden and also of lesser far-field R wave sensing and greater lead stability. Second, a postmortem series examining the number, size, and site of LA veins draining into the proximal CS is described. LA vein pacing was successful in 9 of 14 patients. LA vein electrode parameters have been stable during a median follow-up of 580 days. There were three early lead dislodgments but no other complications. In the second study, a postmortem analysis of 43 human hearts was performed. The study found that 38 (88.4%) of 43 hearts had at least one LA vein draining into the proximal 5 cm of the CS. In addition, 81.2% (33/43) had at least one vein greater than 4 Fr caliber. Thus, pacing in a greater proportion of patients might be achieved by the development and use of smaller (3, 4, and 5 Fr) electrodes. Furthermore, these smaller leads would obviously allow deeper advancement into the LA veins with the potential advantages of greater interatrial synchronization and lead stability and lesser far-field R wave sensing.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".