THE IMPACT OF CARDIAC REHABILITATION ON FRAILTY SEVERITY
Bibliographic record
Abstract
We aimed to determine if cardiac rehabilitation reduces frailty levels from admission to program completion. Data from 250 patients with stable cardiovascular disease referred for rehabilitation were analyzed. Participants engaged in a 12-week group-based exercise and education program 2x/week. Frailty was measured with a 24-item frailty index. Frailty severity was assessed using clinically meaningful frailty index cut-offs (<0.20, 0.20–0.30, 0.30–0.40, and >0.40). Improvement in frailty was defined as a reduction in frailty index scores by ≥0.03 (minimal important difference). Frailty levels were calculated in 179 patients at baseline and program completion (mean age: 61.7 ± 11.0; 27.5% female); 4.5%. 26.3%, 25.7%, and 43.6% met the <0.20, 0.20–0.30, 0.30–0.40, and >0.40 frailty severity categories, respectively. Frailty scores were significantly reduced from baseline (0.38 ± 0.12) to program completion (0.31 ± 0.12; p<0.0001); 66% of participants improved their frailty scores. Compared to participants in the two lowest frailty severity groups, age and sex-adjusted linear regression models demonstrated that patients with a frailty index score of 0.30–0.4 did not have a greater relative improvement in frailty at the end of the program; whereas the highest severity group (>0.40) had a significantly greater relative change (β-coefficient: -0.07, 95% CI: -0.04 to -0.10; p<0.0001). Improvement in a minimal important frailty index score change at program completion was achieved by 25%, 53%, 70%, and 76%, across the frailty severity categories, respectively (p<0.0001). Cardiac rehabilitation can significantly reduce frailty levels in patients with cardiovascular disease (by ~1.7 fewer deficits). Patients with more severe frailty levels may derive the most benefit from cardiac rehabilitation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".