Abstract 12047: Quality of Life in Adults With Congenital Heart Disease Worldwide: A Multilevel Study in 15 Countries
Bibliographic record
Abstract
Objectives: Health care should be evaluated not only on the basis of saving lives, but also in terms of improving the quality of life (QOL). However, results regarding QOL in adults with congenital heart disease (CHD) are inconsistent and vary across the world. Methodological differences and limitations hamper the interpretation of these results. Therefore, we aimed (1) to describe QOL in an international sample of adults with CHD using a uniform method; (2) to investigate the association between patient characteristics and variation in QOL; and (3) to explore international variation in QOL and the relationship with country-specific characteristics. Methods: For this cross-sectional multilevel study, patients were recruited from adult CHD centers or national registries from 15 countries. QOL was assessed by a linear analog scale (0-100). Patient characteristics included sex, age, marital status, educational level, employment status, CHD complexity and patient-reported New York Heart Association (NYHA) class. Country-specific characteristics included national happiness score and six cultural dimensions. Multilevel generalized linear mixed models were conducted and Empirical Bayes estimates for QOL were calculated to account for the sample size in each country. Results: In total, 4,028 adults with CHD were enrolled (median age=32 y; 53% women; 26% mild, 49% moderate, 26% complex CHD). Median QOL for the total sample was 80. Age, marital status, employment status and NYHA class were significantly associated with variation in QOL (p<.001). Australia had the highest QOL estimate (82) and Japan the lowest (72). Four countries had a QOL estimate of ≥80 (i.e., Australia, Switzerland, USA, Malta). After adjusting for patient characteristics, happiness and cultural dimensions were not significantly associated with variation in QOL. Conclusions: This is the first international study comprehensively assessing QOL in any subgroup of cardiac patients. We found that QOL in adults with CHD varies across countries and is related to patient characteristics but not to country-specific characteristics. The identification of significant patient characteristics can support the development of interventions targeting the QOL of adults with CHD around the world.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".