Chronic heart failure management in adult patients with congenital heart disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: A growing number of adult patients with congenital heart disease (ACHD) are entering the healthcare system as a result of advances in the diagnosis and management of congenital heart defects. Heart failure is a common final pathway for this diverse patient population, representing the leading cause of mortality in ACHD patients. Herein, we review present guideline-directed management of heart failure in ACHD patients. RECENT FINDINGS: There exists a dearth of data to guide management of ACHD-related heart failure. Given this gap, recent guidelines have been limited in the recommendations they can provide for this patient population, with practitioners being consequently forced to generalize findings from studies of acquired heart disease patients based on mechanistic plausibility. The small number of studies directly assessing ACHD patients have been largely limited in their clinical relevance through being negative, small, observational, limited to specific subsets of ACHD patients or assessing nonvalidated outcomes. SUMMARY: Despite the prevalence and impact of ACHD-related heart failure, there are limited evidence-based therapies for its management. Given the rising burden of this clinical problem, definitive trials assessing newer therapies are required to establish their potential role in heart failure amongst ACHD patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".