Compensatory stepping responses in individuals with stroke: A pilot study
Bibliographic record
Abstract
Impaired postural control and a high incidence of falls are commonly observed following stroke. Compensatory stepping responses are critical to reactive balance control. We hypothesize that, following a stroke, individuals with unilateral limb dyscontrol will be faced with the unique challenge of controlling such rapid stepping reactions that may eventually be linked to the high rate of falling. The objectives of this exploratory pilot study were to investigate compensatory stepping in individuals poststroke with regard to: (1) choice of initial stepping limb (paretic or non-paretic); (2) step characteristics; and (3) differences in step characteristics when the initial step is taken with the paretic vs. the non-paretic limb. Four subjects following stroke (38-165 days post) and 11 healthy young adults were recruited. Anterior and posterior perturbations were delivered by using a weight drop system. Force plates recorded centre-of-pressure excursion prior to the onset of stepping and step timing. Of the four subjects, three only attempted to step with their non-paretic limb and one stepped with either limb. Time to foot-off was generally slow, whereas step onset time and swing time were comparable to healthy controls. Two of the four subjects executed multistep responses in every trial, and attempts to force stepping with the paretic limb were unsuccessful in three of the four subjects. Despite high clinical balance scores, these individuals with stroke demonstrated impaired compensatory stepping responses, suggesting that current clinical evaluations might not accurately reflect reactive balance control in this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".