Physiologic VDD Versus Nonphysiologic VVI Pacing In Canine 3rd-Degree Atrioventricular Block
Bibliographic record
Abstract
Historically, ventricular demand, nonphysiologic (VVI) pacing has been the most commonly used modality to treat 3rd-degree atrioventricular (AV) block. The goal of this study was to determine the feasibility of using a commercial, single-lead, physiologic (VDD) pacemaker in dogs with 3rd-degree AV block. Furthermore, we hoped to characterize and identify differences in the radiographic, echocardiographic, neurohormonal, and quality of life consequences of physiologic versus nonphysiologic pacing. We evaluated 10 dogs during a 12-week crossover study. Acutely, rate-matched physiologic pacing reduced pulmonary capillary wedge pressure by 19% compared with nonphysiologic pacing. VDD pacing significantly reduced left atrial size normalized to body weight, left atrial-to-aortic root ratio, and left ventricular end-systolic dimension and increased fractional shortening, aortic Doppler velocity, cardiac output, and stroke volume compared with VVI pacing. Variable rate VDD pacing resulted in a significantly slower heart rate (HR) during echocardiography than fixed-rate (100 bpm) VVI pacing. AV synchronous pacing reduced circulating N-terminal proatrial natriuretic peptide (ANP), norepinephrine (NOR), and epinephrine (EPI) concentrations compared with asynchronous pacing. There were no significant differences in systemic blood pressure, thoracic radiographs, or owner-perceived quality of life. The median percentage of AV synchronous pacing during the VDD modality was 99.8% (range, 1.2 to 99.9%). This study confirms the potential to achieve physiologic pacing with a commercial, single-lead system in dogs. VDD pacing improved hemodynamics and neurohormonal profiles over asynchronous pacing although the long-term clinical benefits of these changes remain to be determined.
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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 | 0.001 |
| 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) | 0.000 | 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".