Effects of passive Bi-axial ankle stretching while walking on uneven terrains in older adults with chronic stroke
Bibliographic record
Abstract
Many people with stroke experience foot drop while walking. Further, walking on uneven surfaces is a common fall risk for these people that hinder with their daily life activities. In addition, a few years after a stroke, lower-limb exercises become less focused, especially the ankle joint movement. The objective of this study is to determine the gait performance of older adults with chronic stroke on an uneven surface in relation to ankle mobility after a four-week bi-axial ankle range of motion (ROM) exercise session. Fifteen older adults with chronic post-stroke hemiparesis (N = 15; mean age = 65 years) participated in a total of 12 bi-axial ankle ROM exercises that consisted of three 30-min training sessions per week for four weeks. Basic clinical tests and gait performance in even and uneven surfaces were evaluated before and after training. Participants with chronic post-stroke hemiparesis showed significantly improved ankle functions, decreased ankle stiffness (from 0.140 ± 0.059 to 0.128 ± 0.067 N·m/°; p = 0.025), and increased paretic ankle passive ROMs (dorsiflexion(DF)/plantarflexion(PF): from 27.3 ± 14.7° to 50.6 ± 10.3°, p < 0.001; inversion(INV)/eversion(EV): 21.7 ± 9.7° to 28.6 ± 9.9°; p = 0.033) after training. They exhibited significant improvements in the walking performance over an uneven surface, step kinematics (walking speed 0.257 ± 0.17 to 0.320 ± 0.178 m/s; p = 0.017; step length: 0.214 ± 0.109 to 0.243 ± 0.108 m; p = 0.009), and clinical balance and mobility (Berg balance scale: 47.2 ± 4.7 to 50.1 ± 3.9, p = 0.0001; timed-up and go test: 23.9 ± 10.3 to 20.2 ± 7.0 s, p = 0.0156). This study is the first research to investigate the walking performance on uneven surfaces in the elderly with chronic stroke in relation to the ankle biomechanical property changes.
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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) | 0.001 | 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".