Contribution of individual and cumulative frailty-related health deficits on cardiac rehabilitation completion
Bibliographic record
Abstract
BACKGROUND: Despite the high burden of frailty among cardiac rehabilitation (CR) participants, it is unclear which frailty-related deficits are related to program completion. METHODS: Data from a single-centre exercise- and education-based CR program were included. A frailty index (FI) based on 25 health deficits was constructed. Logistic regression was used to estimate the odds of CR completion based on the presence of individual FI items. The odds of completion for cumulative deficits related to biomarkers, body composition, quality of life, as well as a composite of traditional and non-traditional cardiovascular risk factor domains were examined. RESULTS: A total of 3,756 individuals were included in analyses. Eight of 25 FI variables were positively associated with program completion while 8 others were negatively associated with completion. The variable with the strongest positive association was the food frequency questionnaire score (OR 1.27 (95% CI 1.14, 1.41), whereas the deficit with strongest negative association was a decline in health over the last year (OR 0.74 (95% CI 0.58, 0.93). An increased number of cardiovascular deficits were associated with an increased odds of CR completion (OR per 1 deficit increase 1.16 (95% CI 1.11, 1.22)). A higher number of traditional CR deficits were predictive of CR completion (OR 1.22 (95% CI 1.16, 1.29)), but non-traditional measures predicted non-completion (OR 0.95 (95% CI 0.92, 0.97)). CONCLUSION: A greater number of non-traditional cardiovascular deficits was associated with non-completion. These data should be used to implement intervention to patients who are most vulnerable to drop out to maximize retention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".