Optimal resistance exercise training parameters for stroke recovery: A protocol for a systematic review
Bibliographic record
Abstract
BACKGROUND: Stroke impacts nearly 14 million people annually. Muscle strength and physical function are often affected by stroke and important determinants of stroke recovery. Resistance exercise training (RT) has been shown to improve muscle strength, but RT prescriptions may be suboptimal for other aspects of stroke recovery. Parameters such as frequency, intensity, type, and duration may influence the effectiveness of RT interventions but have not been systematically evaluated. OBJECTIVES: 1) To determine the effects of RT on stroke recovery, and 2) to examine the influence of RT parameters on intervention effects. ELIGIBILITY CRITERIA: Randomized controlled trials examining the effects of RT will be eligible for this systematic review if they: 1) included only adults with stroke or transient ischemic attack, 2) compared RT to no exercise or usual care, and 3) did not apply a co-intervention. STUDY SELECTION: Eight databases (MEDLINE, EMBASE, EMCARE, AMED, PsychINFO, CINAHL, SPORTDiscus, and Web of Science) and 2 clinical trials registries (ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform) will be searched from inception. Two independent pairs of authors will compare titles, abstracts, and full-text reports against the eligibility criteria. Conflicts will be resolved by consensus or third author. MAIN OUTCOME MEASURES: The construct of interest is stroke recovery. An advisory group of clinicians, researchers, and partners with lived experience of stroke will be consulted to determine specific outcome measures of interest, and to rank their relative importance. We expect to include measures of physical function, strength, cognition, and quality of life. Random-effects meta-analyses will be used to pool results for each outcome across studies, and RT parameters (frequency, intensity, type, and duration) will be used as covariates in meta-regression analyses. CONCLUSION: The results of this review will inform the optimal RT prescription parameters for promoting stroke recovery.
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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 | 0.074 | 0.094 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.018 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.065 | 0.008 |
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".