Sincronia Ventricular na Estimulação Cardíaca Parahissiana: Alternativa por Ativação Cardíaca Fisiológica (Estimulação Indireta do Feixe de His)?
Bibliographic record
Abstract
BACKGROUND: Artificial cardiac pacing by direct or indirect His bundle capture results in synchronous ventricular contraction (physiological pacing). OBJECTIVES: To compare cardiac synchronization, technical characteristics, and electronic parameters between two techniques of indirect His-bundle pacing: non-selective (NS-HBP) vs para-Hisian pacing (PHP). METHODS: The experimental intervention (between November 2019 and April 2020) consisted of implanting a DDD pacemaker in patients who had left ventricular ejection fraction (LVEF) > 35%. The resulting cardiac synchronization was compared using an electrocardiographic algorithm that analyzed QRS variation and the technical characteristics of non-selective Hisian pacing (DDD-His) and para-Hisian pacing (DDD-Var). RESULTS: Of 51 total patients (men: 28), 66.7% (34) were allocated to the DDD-Var group and 33.3% (17) to the DDD-His group. The mean ages in each group were 74 and 79 years, respectively. In the DDD-Var group, QRS variation (ventricular synchrony) improved after implantation (p < 0.001). In post-implantation ECG, 91.2% of the DDD-Var group presented a physiological pacing pattern, which was similar to the DDD-His group (88.2%; p = 0.999). The paced QRS axis was also similar (physiological) for both groups. Intraoperative fluoroscopy time (XRay) during implantation was lower for the para-Hisian technique (median 7 min in the DDD-Var group vs 21 min in the DDD-His group, p < 0.001). The mean QRS duration increased in the DDD-Var group (114.7 ms pre-implantation vs 128.2 ms post-implantation, p = 0.044). The mean post-implantation R-wave amplitude was 11.2 mV in the DDD-Var group vs 6.0 mV in the DDD-His group, p = 0.001. CONCLUSION: Para-Hisian pacing appears to indirectly recruit the His bundle, which would make this an effective and comparable strategy for physiological pacing, resulting in synchronous ventricular contraction similar to that of non-selective Hisian pacing.
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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 | 0.006 |
| 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".