Effectiveness of a rehabilitation dog in fostering gait retraining for adults with a recent stroke: A multiple single-case study
Bibliographic record
Abstract
INTRODUCTION: Gait retraining approaches and walking aids currently used for patients with recent stoke present limitations regarding their utilization in clinical and real life settings. A rehabilitation facility implemented an innovative approach using a rehabilitation dog for gait retraining and as a walking aid. OBJECTIVE: To explore the effectiveness of rehabilitation dog, both as a gait retraining approach and as a walking aid, to improve walking speed and gait pattern. METHODS: Four participants, post recent stroke with hemiparesis, were recruited in a rehabilitation unit. A multiple single-case ABA design was used. The three-week training period focused on gait retraining using a rehabilitation dog. For each session, walking speed with the rehabilitation dog and the cane was documented. In each phase (ABA), the gait pattern was analyzed. RESULTS: Mean (SD) age of the participants was 58 (3.2) years old; time since stroke was 88 (61.41) days. For three participants, walking speed was significantly increased after retraining with the dog. The four participants showed improvement of their gait pattern across phases and walked significantly faster with the dog than with the cane. CONCLUSION: This pilot study suggests that a rehabilitation dog is an interesting gait retraining approach for a clinical setting and an effective walking aid following stroke.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".