Exercise-Based Stroke Rehabilitation: Clinical Considerations Following the COVID-19 Pandemic
Bibliographic record
Abstract
Background. The COVID-19 pandemic attributable to the severe acute respiratory syndrome virus (SARS-CoV-2) has had a significant and continuing impact across all areas of healthcare including stroke. Individuals post-stroke are at high risk for infection, disease severity, and mortality after COVID-19 infection. Exercise stroke rehabilitation programs remain critical for individuals recovering from stroke to mitigate risk factors and morbidity associated with the potential long-term consequences of COVID-19. There is currently no exercise rehabilitation guidance for people post-stroke with a history of COVID-19 infection. Purpose. To (1) review the multi-system pathophysiology of COVID-19 related to stroke and exercise; (2) discuss the multi-system benefits of exercise for individuals post-stroke with suspected or confirmed COVID-19 infection; and (3) provide clinical considerations related to COVID-19 for exercise during stroke rehabilitation. This article is intended for healthcare professionals involved in the implementation of exercise rehabilitation for individuals post-stroke who have suspected or confirmed COVID-19 infection and non-infected individuals who want to receive safe exercise rehabilitation. Results. Our clinical considerations integrate pre-COVID-19 stroke (n = 2) and COVID-19 exercise guidelines for non-stroke populations (athletic [n = 6], pulmonary [n = 1], cardiac [n = 2]), COVID-19 pathophysiology literature, considerations of stroke rehabilitation practices, and exercise physiology principles. A clinical decision-making tool for COVID-19 screening and eligibility for stroke exercise rehabilitation is provided, along with key subjective and physiological measures to guide exercise prescription. Conclusion. We propose that this framework promotes safe exercise programming within stroke rehabilitation for COVID-19 and future infectious disease outbreaks.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".