The Relationship of Lower-Extremity Muscle Torque to Locomotor Performance in People With Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Improved walking is a common goal after stroke. The purpose of this study was to examine the relationship between the torque generated by the muscles of both lower extremities and 2 locomotor tasks: gait on level surfaces and stair climbing in people who had strokes. SUBJECTS: Twenty community-dwelling individuals (mean age=61.2 years, SD=8.4, range=52-82) who had strokes and who were able to walk independently participated in the study. The mean time since stroke was 4.0 years (SD=2.6, range=1.5-10.0). METHODS: Pearson correlations and multiple regression were used to measure the relationship between concentric isokinetic torque of the flexor and extensor muscles of the hip, knee, and ankle bilaterally and locomotor performance (gait on level surfaces and stair-climbing speed). RESULTS: The isokinetic torques of the paretic ankle plantar flexors, hip flexors, and knee flexors had moderate to high correlations (r=.5-.8) with gait and stair-climbing speeds. Muscle force could explain 66% to 72% of the variability in gait and stair-climbing speeds. Correlations for the nonparetic side were as high as or higher than those for the paretic side for some muscle groups. DISCUSSION AND CONCLUSION: Muscle performance measurements of both limbs should be included in the evaluation of locomotion and treatment of people following a stroke.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".