Effect of a Foot-Drop Stimulator and Ankle–Foot Orthosis on Walking Performance After Stroke
Bibliographic record
Abstract
BACKGROUND: Studies have demonstrated the efficacy of functional electrical stimulation in the management of foot drop after stroke. OBJECTIVE: To compare changes in walking performance with the WalkAide (WA) foot-drop stimulator and a conventional ankle-foot orthosis (AFO). METHODS: Individuals with stroke within the previous 12 months and residual foot drop were enrolled in a multicenter, randomized controlled, crossover trial. Subjects were assigned to 1 of 3 parallel arms for 12 weeks (6 weeks/device): arm 1 (WA-AFO), n = 38; arm 2 (AFO-WA), n = 31; arm 3 (AFO-AFO), n = 24. Primary outcomes were walking speed and Physiological Cost Index for the Figure-of-8 walking test. Secondary measures included 10-m walking speed and perceived safety during this test, general mobility, and device preference for arms 1 and 2 for continued use. Walking tests were performed with (On) and without a device (Off) at 0, 3, 6, 9, and 12 weeks. RESULTS: Both WA and AFO had significant orthotic (On-Off difference), therapeutic (change over time when Off), and combined (change over time On vs baseline Off) effects on walking speed. An AFO also had a significant orthotic effect on Physiological Cost Index. The WA had a higher, but not significantly different therapeutic effect on speed than an AFO, whereas an AFO had a greater orthotic effect than the WA (significant at 12 weeks). Combined effects on speed after 6 weeks did not differ between devices. Users felt as safe with the WA as with an AFO, but significantly more users preferred the WA. CONCLUSIONS: Both devices produce equivalent functional gains.
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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 | 0.002 |
| 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 | 0.001 |
| 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".