Relationships Between Stepping-Reaction Movement Patterns and Clinical Measures of Balance, Motor Impairment, and Step Characteristics After Stroke
Bibliographic record
Abstract
OBJECTIVE: Successful stepping reactions, led by either the paretic or nonparetic leg, in response to a loss of balance are critical to safe mobility poststroke. The purpose of this study was to measure sagittal plane hip, knee, ankle, and trunk kinematics during 2-step stepping reactions initiated by paretic and nonparetic legs of people who had stroke and members of a control group. METHODS: Principal component analysis (PCA) was used to reduce the data into movement patterns explaining interlimb coordination of the stepping and stance legs. Correlations among principal components loading scores and clinical measures of balance ability (as measured on the Community Balance and Mobility scale), motor impairment (as measured on the foot and leg sections of the Chedoke-McMaster Stroke Assessment), and step characteristics (length and velocity) were used to examine the effect of stroke on stepping reaction movement patterns. RESULTS: The first 5 principal components explained 95.9% of the movement pattern of stepping reactions and differentiated between stepping reactions initiated by paretic legs, nonparetic legs, or the legs of controls. Moderate-strong associations (ρ/r > 0.50) between specific principal component loading scores and clinical measures and step characteristics were dependent on the initiating leg. Lower levels of motor impairment, higher levels of balance ability, and faster and longer steps were associated with stepping reactions initiated by the paretic leg that comprised paretic leg flexion and nonparetic leg extension. Step initiation with the nonparetic leg showed associations between higher scores on clinical measures and movement patterns of flexion in both paretic and nonparetic legs. CONCLUSIONS: Movement patterns of stepping reactions poststroke were influenced by the initiating leg. After stroke, specific movement patterns showed associations with clinical measures depending on the initiating leg, suggesting that these movement patterns are important to retraining of stepping reactions. Specifically, use of flexion patterning and assessment of between-leg pattern differentiation may be important aspects to consider during retraining of stepping reactions poststroke. IMPACT: Evidence-based interventions targeting balance reactions are still in their infancy. This investigation of stepping reactions poststroke addresses a major gap in research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".