Neuromuscular strategies in the paretic leg during curved walking in individuals post-stroke
Bibliographic record
Abstract
Reduced flexibility over the neuromotor control of paretic leg muscles may impact the extent to which individuals post-stroke modulate their muscle activity patterns to walk along curved paths. The purpose of this study was to compare lower-limb movements and neuromuscular strategies in the paretic leg of individuals with stroke with age-matched controls during curved walking. Participants walked at their preferred walking velocity along four different paths of increasing curvature, while lower-limb kinematics and muscle activity were recorded. A second group of able-bodied individuals walked along the four paths, matching the walking speed of the stroke group. The stroke group showed reduced lower-limb joint excursion and disordered modulation of foot pressure during curved walking, accompanied by reduced modulation of muscle activity patterns. In the inner leg of the curve in control subjects, the posteromedial muscles (medial gastrocnemius and medial hamstrings) showed decreased electromyographic amplitude as path curviture increased. Conversely, activity of the posterolateral musculature of the outer leg was decreased with increasing path curvature. Activity in the tibialis anterior and gluteus medius was also modulated with path curvature. However, in the stroke group, we found reduced modulation of muscle activity in the paretic leg during curved walking. The extent of modulation was also associated with the level of physical impairment due to stroke. The results of this study provide further knowledge about neuromuscular control of locomotor adaptations post-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 | 0.000 |
| 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) | 0.001 | 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".