Examining Differences in Patterns of Sensory and Motor Recovery After Stroke With Robotics
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Developing a better understanding of the trajectory and timing of stroke recovery is critical for developing patient-centered rehabilitation approaches. Here, we quantified proprioceptive and motor deficits using robotic technology during the first 6 months post stroke to characterize timing and patterns in recovery. We also make comparisons of robotic assessments to traditional clinical measures. METHODS: One hundred sixteen subjects with unilateral stroke were studied at 4 time points: 1, 6, 12, and 26 weeks post stroke. Subjects performed robotic assessments of proprioceptive (position sense and kinesthesia) and motor function (unilateral reaching task and bimanual object hit task), as well as several clinical measures (Functional Independence Measure, Purdue Pegboard, and Chedoke-McMaster Stroke Assessment). RESULTS: One week post stroke, many subjects displayed proprioceptive (48% position sense and 68% kinesthesia) and motor impairments (80% unilateral reaching and 85% bilateral movement). Interindividual recovery on robotic measures was highly variable. However, we characterized recovery as early (normal by 6 weeks post stroke), late (normal by 26 weeks post stroke), or incomplete (impaired at 26 weeks post stroke). Proprioceptive and motor recovery often followed different timelines. Across all time points, robotic measures were correlated with clinical measures. CONCLUSIONS: These results highlight the need for more sensitive, targeted identification of sensory and motor deficits to optimize rehabilitation after stroke. Furthermore, the trajectory of recovery for some individuals with mild to moderate stroke may be much longer than previously considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".