Phenotypes of adults with congenital heart disease around the globe: a cluster analysis
Bibliographic record
Abstract
Abstract Objective To derive cluster analysis-based groupings for adults with congenital heart disease (ACHD) when it comes to perceived health, psychological functioning, health behaviours and quality of life (QoL). Methods This study was part of a larger worldwide multicentre study called APPROACH-IS; a cross sectional study which recruited 4028 patients (2013–2015) from 15 participating countries. A hierarchical cluster analysis was performed using Ward's method in order to group patients with similar psychological characteristics, which were defined by taking into consideration the scores of the following tests: Sense Of Coherence, Health Behavior Scale (physical exercise score), Hospital Anxiety Depression Scale, Illness Perception Questionnaire, Satisfaction with Life Scale and the Visual Analogue Scale scores of the EQ-5D perceived health scale and a linear analogue scale (0–100) measuring QoL. Results 3768 patients with complete data were divided into 3 clusters. The first and second clusters represented 89.6% of patients in the analysis who reported a good health perception, QoL, psychological functioning and the greatest amount of exercise. Patients in the third cluster reported substantially lower scores in all PROs. This cluster was characterised by a significantly higher proportion of females, a higher average age the lowest education level, more complex forms of congenital heart disease and more medical comorbidities. Conclusions This study suggests that certain demographic and clinical characteristics may be linked to less favourable health perception, quality of life, psychological functioning, and health behaviours in ACHD. This information may be used to improve psychosocial screening and the timely provision of psychosocial care.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".