Prediction of Stroke Motor Recovery Using Reflex Stiffness Measures at One Month
Bibliographic record
Abstract
This study characterizes the recovery patterns of motor impairment after stroke, and uses neuromuscular measures of the elbow joint at one month after the event to predict the ensuing recovery patterns over 12 months. Motor impairment was assessed using the Fugl-Meyer Assessment (FMA) of the upper extremity at various intervals after stroke. A parallel-cascade system identification technique characterized the intrinsic and reflex stiffness at various elbow angles. We then used "growth-mixture" modeling to identify three distinct recovery classes for FMA. While class 1 and class 3 subjects both started with low FMA, those in class 1 increased FMA significantly over 12-month recovery period, whereas those in class 3 presented no improvement. Class 2 subjects started with high FMA and also exhibited significant FMA improvement, but over a smaller range and at a slower recovery rate than class 1. Our results showed that the one-month reflex stiffness was able to distinguish between classes 1 and 3 even though both showed similarly low month-1 FMA. These findings demonstrate that, using reflex stiffness, we were able to accurately predict arm function recovery in stroke subjects over one year and beyond. This information is clinically significant and can be helpful in developing targeted therapeutic interventions.
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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".