Robotic Characterization of Ipsilesional Motor Function in Subacute Stroke
Bibliographic record
Abstract
BACKGROUND: Poststroke impairments of the ipsilesional arm are often discussed, but rarely receive focused rehabilitation. Ipsilesional deficits may affect daily function and although many studies have investigated them in chronic stroke, few characterizations have been made in the subacute phase. Furthermore, most studies have quantified ipsilesional deficits using clinical measures that can fail to detect subtle, but important deficits in motor function. OBJECTIVE: We aimed to quantify reaching deficits of the contra- and ipsilesional limbs in the subacute phase poststroke. METHODS: A total of 227 subjects with first-time, unilateral stroke completed a unilateral assessment of motor function (visually guided reaching) using a KINARM robot. Subjects completed the task with both the ipsi- and contralesional arms. Subjects were assessed on a variety of traditional clinical measures (Functional Independence Measure, Chedoke-McMaster Stroke Assessment, Purdue Pegboard, Behavioral Inattention Test) to compare with robotic measures of motor function. RESULTS: Ipsilesional deficits were common and occurred in 37% (n = 84) of subjects. Impairments of the ipsilesional and contralesional arm were weakly to moderately correlated on robotic measures. Magnitude of impairment of the contralesional arm was similar for subjects with and without ipsilesional deficits. Furthermore, we found that a higher percentage of subjects with right-hemisphere stroke had ipsilesional deficits and more subjects with left-hemisphere subcortical strokes did not have ipsilesional deficits. CONCLUSIONS: Magnitude of contralesional impairment and lesion location may be poor predictors of individuals with ipsilesional impairments after stroke. Careful characterization of ipsilesional deficits could identify individuals who may benefit from rehabilitation of the less affected arm.
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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 | 0.001 |
| 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".