Quantitative assessment of motor-proprioceptive deficits in nonparetic arm after stroke via a two-DOF passive manipulandum
Bibliographic record
Abstract
Stroke is the leading cause of motor-proprioceptive in stroke survivors. The stroke rehabilitation is primarily focused on the paretic arm to achieve functional tasks. Only handful studies have targeted the non-paretic arm to understand its impact on the stroke rehabilitation. Hence, to further the literature, we attempted to quantify and compare the proprioceptive loss and motor pattern in both non-paretic and paretic arms after stroke. Here, we first present two degrees of freedom (DOF) manipulandum to quantify motor-proprioceptive deficits and next we present the motor behavior of both paretic and non-paretic arms after stroke as compared to healthy individuals. Ten healthy participants and seven stroke patients joined the study. Results showed that for healthy participants there was no statistically significant difference of the final position accuracy between the two arms in reaching different targets. On contrary, for stroke patients, there was a statistically significant difference between the two arms performance and in reaching different targets by either paretic arms (non-dominant) and non-paretic arms (dominant) in the workspace. Interestingly, stroke patients' non-paretic dominant arms did not follow the same motor pattern as for healthy participants. Altogether, we report significant loss of motor-proprioception in non-paretic arm in addition to paretic arm deficits after stroke. Therefore, we suggest that clinicians should consider non-paretic arm during rehabilitation to gain maximal recovery after stroke. Future investigations should be performed to consolidate these findings with a larger population size.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".