What factors are associated with patients walking fitness when starting cardiac rehabilitation?
Bibliographic record
Abstract
INTRODUCTION: Patients with heart failure (HF) are now a priority group for cardiac rehabilitation (CR). A fundamental component of CR is increasing physical fitness through exercise training. Even though studies show fitness benefits, there is little evidence in routine populations of individual factors that may influence fitness. This study aims to evaluate the extent to which demographics and clinical measures predict physical fitness in patients with HF and develop reference values to guide practice. METHODS: Data from the National Audit Cardiac Rehabilitation (NACR) was analysed. 2047 patients (73% male) with HF completed an incremental shuttle walk test (ISWT). Backward regression accounting for patient characteristics and new comorbidity groups were used to identify predictors of distance using ISWT. Reference values were produced from the percentiles of the ISWT distance. RESULTS: value < 0.001). Diuretics (-33.01 m ±8.07 SD) and socioeconomic status (9.12 m ±2.91 SD) were significant predictors of baseline walking fitness. Furthermore, respiratory obstructions, musculoskeletal issues and metabolic diseases were associated with reduced walking distance of 29.8 m, 26.6 m and 18.4 m respectively. CONCLUSION: Use of diuretics, socioeconomic status and presence of comorbidities were significant predictors of walking performance in patients with HF who attended CR and were fit enough to carry out an ISWT. Reference values, to aid clinical practice, were developed that included age, gender and comorbidities status.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".