Effects of Passive Leg Movement on the Oxygenation Level of Lower Limb Muscle in Chronic Stroke Patients
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of passive leg movements on the muscle oxygenation level and electromyographic (EMG) activity in the lower limbs in chronic stroke patients. METHODS: With a gait training apparatus, passive movements were imposed on the lower limbs of 15 chronic stroke patients at a frequency of 0.8 Hz for 10 minutes. During the passive leg movements, muscle oxygenation level and muscular EMG activity of the paretic and nonparetic calf muscles were assessed. RESULTS: The passive leg movements caused increases in the EMG activity and muscle oxygenation level in both paretic and nonparetic lower limbs. Although a significant difference was found in the concentration changes of the oxygenated hemoglobin (Oxy-Hb), both paretic and nonparetic sides of the muscle showed enhancement of the tissue oxygenation level (TOI). The degree of the changes of the Oxy-Hb depended on the level of motor recovery after stroke; subjects with good motor recovery showed less difference in the Oxy-Hb level between the paretic and nonparetic sides of the muscle. CONCLUSION: Passive leg movements have the capacity to induce muscular activity and enhance oxygen metabolism, even in the paretic lower limb muscle of chronic stroke patients. This type of exercise might be a useful and efficient method for the prevention of metabolic deterioration in the lower limb paretic muscles of chronic stroke patients.
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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".