Cardiovascular Fitness and Adaptations to Aerobic Training after Stroke
Bibliographic record
Abstract
Purpose: This article presents an overview of the current state of knowledge of physiological indicators of cardiovascular fitness after stroke. Clinical tips are presented regarding training protocols appropriate for persons post-stroke. Summary of Key Points: Application of the principles of exercise physiology to stroke rehabilitation has begun to attract the attention of clinicians owing to increased awareness of the profoundly poor fitness levels of persons in the early and chronic post-stroke periods. Contributors to the low exercise capacity range from personal and environmental factors to cardiovascular, neuromuscular, and respiratory dysfunction associated with stroke. Low fitness levels, in turn, negatively influence these factors and, ultimately, health-related quality of life. Nevertheless, there is growing evidence that persons in both the early and chronic post-stroke periods can make cardiovascular adaptations to aerobic training. Conclusions: Given that persons post-stroke respond positively to aerobic exercise if appropriate screening and training protocols are used, implementation of training makes practical sense. However, there is limited information on specific evidence-based guidelines for stroke rehabilitation in clinical and community settings to improve exercise capacity and to protect against further cardiovascular morbidity and mortality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".