Quality of life in children and young adults with cardiac conditions
Bibliographic record
Abstract
PURPOSE OF REVIEW: Unequivocal conclusions regarding the quality of life (QOL) of children, adolescents, and young adults with heart disease cannot be drawn because results vary across studies and between patient and parent-proxy reports. This review focuses on the recent studies that help us understand this variability and why subgroups of young cardiac patients do appear at increased risk of impaired QOL. RECENT FINDINGS: The age at which QOL is assessed might contribute to variability in reported QOL outcomes, with the greatest QOL impairment tending to occur with the youngest patients. Adolescents and young adults with heart disease often report positive QOL outcomes. Recent studies have furthered our understanding of the determinants of impaired QOL, including low social support, emotional instability, developmental disabilities, poorer subjective health status, and lower exercise capacity. Certain cardiac treatments, such as transplantation and implantable cardioverter defibrillator implantation, warrant special attention for potential QOL impairment. SUMMARY: Future research should move beyond cross-sectional studies and include longitudinal assessment of QOL. In addition, given the success with extending the lives of young patients with cardiac conditions, it is important that strategies focused on patient and family QOL are also advanced.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.003 | 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".