Comparing Virtual and Center-Based Cardiac Rehabilitation on Changes in Frailty
Bibliographic record
Abstract
Many patients with cardiovascular disease (CVD) are frail. Center-based cardiac rehabilitation (CR) can improve frailty; however, whether virtual CR provides similar frailty improvements has not been examined. To answer this question, we (1) compared the effect of virtual and accelerated center-based CR on frailty and (2) determined if admission frailty affected frailty change and CVD biomarkers. The virtual and accelerated center-based CR programs provided exercise and education on nutrition, medication, exercise safety, and CVD. Frailty was measured with a 65-item frailty index. The primary outcome, frailty change, was analyzed with a two-way mixed ANOVA. Simple slopes analysis determined whether admission frailty affected frailty and CVD biomarker change by CR model type. Our results showed that admission frailty was higher in center-based versus virtual participants. However, we observed no main effect of CR model on frailty change. Results also revealed that participants who were frailer at CR admission observed greater frailty improvements and reductions in triglyceride and cholesterol levels when completing virtual versus accelerated center-based CR. Even though both program models did not change frailty, higher admission frailty was associated with greater frailty reductions and change to some CVD biomarkers in virtual CR.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".