Implantable cardioverter-defibrillators in congenital heart disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Sudden cardiac death is a leading cause of mortality in patients with congenital heart disease (CHD), such that implantable cardioverter-defibrillators (ICDs) are a critical component of care. Objectives of this review are to highlight recent advances regarding ICDs in CHD, with a focus on clinical indications, technical issues and solutions, and patient outcomes. RECENT FINDING: Evolving ICD indications in CHD are primarily derived from observational research or extrapolations from the general adult literature. Technical challenges to device implantation include obstructed vasculature or conduits, intracardiac shunts with their attendant risk for systemic thromboemboli, and lack of venous access to the heart. In selected patients, tailored epicardial systems may be considered that include subcutaneous, retrocardiac, and/or venous (e.g., azygous) coils. Alternatively, an entirely subcutaneous ICD may be a reasonable option in patients with no bradycardia or antitachycardia pacing indications. Long-term complications include inappropriate shocks, lead failure, reduction in quality of life, shock-related anxiety, and impaired sexual function. SUMMARY: Although ICDs undeniably save lives, challenges to applying this technology to patients with CHD include the paucity of evidence-based data to guide patient selection, technical challenges related to venous access, patient size, anatomic complexities, and a high rate of complications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".