Benefits of Cardiac Rehabilitation: Mechanisms to Restore Function and Clinical Impact
Bibliographic record
Abstract
Cardiac rehabilitation (CR) has evolved from foundations as a postmyocardial infarction mobilization strategy for patients who were typically unstable, into a comprehensive, multidisciplinary program for most patients with cardiovascular disease aimed at optimizing cardiovascular health, reducing morbidity, and enhancing functional recovery. Although contemporary CR patients are now usually more stable from a cardiovascular perspective, needs have expanded for comprehensive approaches to exercise, lifestyle, care coordination, risk factor modification, and stress management. Furthermore, contemporary CR patients now typically include older adults who are contending with cardiovascular disease in the context of multimorbidity, frailty, sarcopenia, sensory limits, and cognitive impairment. The physiological mechanisms underlying exercise intolerance in cardiovascular disease include impairments in cardiac output, vascular function, and skeletal muscle metabolism and relate to elemental biological mechanisms that are common to all 3 as well as to noncardiovascular disease and aging. CR provides an important opportunity to address such aggregate risk. Nonetheless, CR remains underutilized, particularly by older adults, women, and those struggling with cognitive impairments, frailty, logistics, and social barriers to care. Emerging strategies, such as home-based and hybrid CR models, resistance training, and digital health technologies, are expanding the potential for access and effectiveness. Future research brings important opportunities to hone personalized CR strategies tailored to contemporary patient populations, including optimized exercise prescriptions as well as pharmacological, nutritional, and technological adjuncts. Related prospects to distinguish the biological mechanisms underlying patient-preferred clinical end points (eg, independence, quality of life) remain critical to augmenting CR's value in the contemporary therapeutic landscape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".