Risk Stratification for Sudden Cardiac Death in Repaired Tetralogy of Fallot
Bibliographic record
Abstract
There has been significant progress in the prevention of sudden cardiac death in repaired tetralogy of Fallot. Contemporary cohorts report greater survival attributable to improved surgical techniques, heart failure management, and proactive strategies for risk stratification and management of ventricular arrhythmias including defibrillator implantation and ablation technology. Over the last 25 years, our understanding of predictive risk factors has also improved from invasive and more limited measures to individualized risk prediction scores based on extensive demographic, imaging, electrophysiological, and functional data. Although each of these contemporary scoring systems improves prediction, there are important differences between the study cohorts, included risk factors, and imaging modalities that can significantly affect interpretation and implementation for the individual patient. In addition, accurate phenotyping of disease complexity and anatomic repair substantially modulates this risk and the mechanism of sudden death. Routine implementation of risk stratification within repaired tetralogy of Fallot management is important and directly informs primary prevention defibrillator implantation as well as consideration for proactive invasive strategies including ventricular tachycardia ablation and pulmonary valve replacement. Assessment and risk stratification by a multidisciplinary team of experts in adult congenital heart disease are crucial and critical. Although we have increased understanding, reconciliation of these complex factors for the individual patient remains challenging and often requires careful consideration and discussion with multidisciplinary teams, patients, and their families.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".