Gait Performance and Lower-Limb Muscle Strength Improved in Both Upper-Limb and Lower-Limb Isokinetic Training Programs in Individuals with Chronic Stroke
Bibliographic record
Abstract
Background. Limited improvement in gait performance has been noted after training despite a significant increase in strength of the affected lower-limb muscles after stroke. A mismatch between the training program and the requirements of gait could explain this finding.Objective. To compare the impact of a training program, matching the requirements of the muscle groups involved in the energy generation of gait, to a control intervention, on gait performance and strength.Methods. 30 individuals with chronic stroke were randomly assigned into two groups (n= 15), each training three times/week for six weeks. The experimental group trained the affected plantarflexors, hip flexors, and extensors, while the control group trained the upper-limb muscles. Baseline and posttraining values of gait speed, positive power (muscles’ concentric action during gait), and strength were retained and compared between groups.Results. After training, both groups showed a similar and significant increase in gait speed, positive power of the hip muscles, and plantarflexors strength.Conclusion. A training program targeting the lower-limb muscles involved in the energy generation of gait did not lead to a greater improvement in gait performance and strength than a training program of the upper-limb muscles. Attending the training sessions might have been a sufficient stimulus to generate gains in the control group.
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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".