From Selective Parasympathetic Modulation of AV Node to Rate Control Therapy
Bibliographic record
Abstract
INTRODUCTION: In pediatric and congenital heart disease patients, transvenous ICD implantation may be limited secondary to patient size, venous, or cardiac anatomy. Epicardial patches require a thoracotomy, and may lead to a restrictive pericardial process. Because of these issues, we have explored novel ICD configurations. METHODS: Retrospective review at 10 centers implanting ICDs without a transvenous shocking coil or epicardial patches. RESULTS: Twenty-two patients underwent implant at a mean age of 8.9 years (range: 0.3-43.5), with a mean weight of 25.5 kg (range: 5.2-70). Diagnoses included complex CHD, intracardiac tumors, cardiomyopathy, idiopathic VT, LV noncompaction, and long QT syndrome. Three configurations were used: subcutaneous array, a transvenous design ICD lead placed on the epicardium, or a transvenous design ICD lead placed subcutaneously. Difficulties were found at implant in 8 patients: 4 had difficulty inducing VT/VF, and 4 had high DFTs. Over a mean follow-up of 2.2 years (range: 0.2-10.5), 7 patients had appropriate shocks. Inappropriate shocks occurred in 4 patients. System revisions were required in 7 patients: 2 generator changes (in 1 patient), 3 pace-sense lead replacement, 1 additional subcutaneous coil placement due to increased DFT, 1 upgrade to a transvenous system, and 1 revision to epicardial patch system. CONCLUSIONS: ICD implantation can be performed without epicardial patches or transvenous high-energy leads in this population, using individualized techniques. This will allow ICD use in patients who have intracardiac shunting or are deemed too small for transvenous ICD leads. The long-term outcome and possible complications are as yet unknown in this population, and they should be monitored closely with follow-up DFTs.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".