Reply to: enhancing cardiac rehabilitation: addressing multidimensional aspects of frailty
Bibliographic record
Abstract
We appreciate the interest by Zhang et al.1 related to our systematic review and meta-analysis on cardiac rehabilitation (CR) and frailty.2 The commentary related to CR delivery models, psychological well-being, and cognitive and social frailties add perspectives that can further advance the field. Here, we acknowledge and build upon their thoughtful comments. Zhang et al.1 propose the study of the heterogeneity of CR program delivery models to address frailty. The emergence of digital and home-based CR programs presents a promising opportunity to improve accessibility and adherence, particularly among frail individuals and those in underserved areas. Notably, only two studies in our review included a virtual CR or home-based program. One study included in our review found that the overall effect of centre- and virtual-based CR programs did not change; however, a mild–moderate frailty levels at CR admission, only the virtually delivered program improved frailty at discharge.3 Therefore, we also support further research comparing the efficacy of traditional, home-based, and digital CR models in frail populations to identify the most effective strategies for improving patient outcomes.
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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 | 0.013 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.031 | 0.033 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".