Evaluation of the Effectiveness of Stepping in Place with Rhythmic Auditory Stimulation on Improving Gait in a Community Dwelling Stroke Population
Bibliographic record
Abstract
Stroke survivors often exhibit asymmetric walking patterns associated with one-sided weakness, or hemiparesis, resulting in a reduced walking ability and lower levels of independence. Rhythmic Auditory Stimulation (RAS) is an emerging strategy that uses music with a strong beat to help cue walking. RAS has successfully improved walking ability and symmetry in stroke survivors, yet the use of RAS while stepping in place has been only minimally explored. Due to limited mobility in this population, stepping in place may be a more feasible method of practicing walking patterns. The purpose of this study is two-fold: 1) to determine the immediate effects of stepping in place with RAS on kinematic walking parameters, and 2)to evaluate whether any changes in gait parameters translate to walking following a RAS session. The study is currently underway, with an objective of testing 8 to 10 participants who have experienced a stroke resulting in hemiparesis and reduced walking ability. The testing protocol is divided into two parts; part one involves an initial walking assessment to determine stepping cadence. In part two, participants complete a gait analysis and functional assessment before and after the stepping in place with RAS session. Two-dimensional analysis using a motion capture system and force plates allows for multiple spatiotemporal and kinematic outcome measures to be collected such as walking speed, step length, and joint angles. Ultimately, findings from this study will help to determine if and how stepping in place with RAS can improve gait in stroke survivors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".