Exploring frailty: muscle strength, functional capacity, activities of daily living and cognition in adult congenital heart disease
Bibliographic record
Abstract
The study aimed to assess frailty in adults with congenital heart disease (ACHD) and to compare muscle strength, functional capacity, activities of daily living (ADL), and cognition between frail and non-frail ACHD patients. A cross-sectional study design was used. Sixty people with ACHD aged between 18 and 45 years were included. Frailty was determined according to the Fried criteria. Peripheral muscle strength was assessed with a digital dynamometer, functional capacity with the 6-min walk test (6MWT), ADL with the Glittre ADL test, and cognition with the Montreal Cognitive Assessment (MoCA) test. Frailty was seen in 38.33% (frail = 23 and non-frail = 37) of the participants. In the frail patients, dominant knee extensor strength (p = 0.002), shoulder abductor strength (p = 0.005), 6MWT distance (p = 0.021), and MoCA score (p = 0.005) were significantly lower than those in the non-frail patients. Glittre ADL test (p = 0.002) was significantly higher in the frail patients. Muscle strength, functional capacity, ADL, functional mobility, and cognition were lower in the frail participants with ACHD. Early assessment of frailty in ACHD and planning individualized exercise training programs for frail individuals may be a strategy to reduce the impact of frailty on adverse clinical outcomes. We found that muscle strength, functional capacity, ADL, functional mobility, physical activity, and cognition were lower in the frail ACHD patients than in the non-frail ones.Early assessment of frailty in individuals with ACHD is important for determining health status.Planning individualized exercise training programs for individuals with ACHD may be a strategy to reduce the impact of frailty on adverse clinical outcomes. We found that muscle strength, functional capacity, ADL, functional mobility, physical activity, and cognition were lower in the frail ACHD patients than in the non-frail ones. Early assessment of frailty in individuals with ACHD is important for determining health status. Planning individualized exercise training programs for individuals with ACHD may be a strategy to reduce the impact of frailty on adverse clinical outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".