Reactive Stepping After Stroke: Determinants of Time to Foot Off in the Paretic and Nonparetic Limb
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Impaired features of reactive stepping, specifically delays in the early time to foot off (TFO) phase, are associated with increased fall rates after stroke. This study aimed to determine differences in, and determinants of, paretic and nonparetic limb TFO, and to determine whether both paretic and nonparetic TFO were associated with perturbation-evoked falls. METHODS: Retrospective chart review of 105 individuals with stroke was performed within an inpatient rehabilitation setting; each had received a standardized assessment of reactive balance control (in response to a perturbation) at time of discharge. RESULTS: There were no significant differences in paretic (351 ms) and nonparetic (365 ms) TFO. The capacity to maximally load the nonparetic limb, the amplitude of the perturbation, and the capacity to load the paretic limb were all negatively associated with paretic step TFO, explaining 23.8% of the variance. The amplitude of the perturbation and the preperturbation load under the nonparetic stepping limb were, respectively, negatively and positively associated with nonparetic step TFO, explaining 22.7% of the variance. The likelihood of a perturbation-evoked fall was associated with mean nonparetic limb TFO but not paretic limb TFO. DISCUSSION AND CONCLUSIONS: Unique stroke-related impairments of dynamic balance control and limb-load asymmetry may differentially influence paretic and nonparetic reactive step TFO, in response to a loss of balance. The amplitude of the perturbation influences reactive step TFO in both limbs. The results of the current study have implications for the future development of standardized clinical assessment methodologies and training strategies to evaluate and remediate reactive stepping and reduce fall risk.Video Abstract available for more insights from the authors (see Supplemental Digital Content 1, http://links.lww.com/JNPT/A133).
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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 | 0.002 |
| 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".