Factors That Influence Muscle Weakness Following Stroke and Their Clinical Implications: A Critical Review
Bibliographic record
Abstract
PURPOSE: To provide a comprehensive review of changes that occur in the muscle after stroke and how these changes influence the force-generating capacity of the muscle. METHODS: A literature search of PubMed, CINAHL, MEDLINE, and Embase was conducted using the search terms stroke, hemiparesis, muscle structure, cross sectional area, atrophy, force, velocity, and torque. There were 27 articles included in this review. RESULTS: Three changes occur in the muscle after stroke: a decrease in muscle mass, a decrease in fibre length, and a smaller pennation angle. In addition, the tendon is stretched and becomes more compliant. All of these factors reduce the affected muscle's ability to generate forces similar to controls or to non-paretic muscles. The result is a leftward shift in the length-tension curve, a downward shift in the torque-angle curve, and a downward shift in the force-velocity curve. CONCLUSION: Changes in muscle architecture contributing to weakness, such as muscle-fibre length, pennation angle, muscle atrophy, and tendon compliance, should be prevented or reversed by means of an appropriate rehabilitation programme.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".