Updates in the management of congenital heart disease in adult patients
Bibliographic record
Abstract
INTRODUCTION: Adults with congenital heart disease represent a highly diverse, ever-growing population. Optimal approaches to management of problems such as arrhythmia, sudden cardiac death, heart failure, transplant, application of advanced therapies and unrepaired shunt lesions are incompletely established. Efforts to strengthen our understanding of these complex clinical challenges and inform evidence-based practices are ongoing. AREAS COVERED: This narrative review summarizes evidence underpinning current approaches to congenital heart disease management while highlighting areas requiring further investigation. A search of literature published in 'Medline,' 'EMBASE,' and 'PubMed' using search terms 'congenital heart disease,' 'arrhythmia,' 'sudden cardiac death,' 'heart failure,' 'heart transplant,' 'advanced heart failure therapy,' 'ventricular assist device (VAD),' 'mechanical circulatory support (MSC),' 'intracardiac shunt' and combinations thereof was undertaken. EXPERT OPINION: Application of novel technologies in the diagnosis and management of arrhythmia has and will continue to improve outcomes in this population. Sudden death remains a prevalent problem with many persistent unknowns. Heart failure is a leading cause of morbidity and mortality. Improved access to specialist care, advanced therapies and cardiac transplant is needed. The emerging field of cardio-obstetrics will continue to define state-of-the-art care for the reproductive health of women with heart disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.007 | 0.002 |
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".