Exaggerated interlimb neural coupling following stroke
Bibliographic record
Abstract
The patterns of interlimb coupling were examined in 10 stroke survivors with chronic hand impairment. In particular, the potential roles of postural state and motor tasks in promoting the flexed posture of the upper extremity were assessed. Through the use of electromyography analysis, joint angle measurements and a novel biomechanical apparatus to perturb the digits of the hand into extension, measurements of muscle activity and joint position were compared during multiple postural states, locomotion and voluntary muscle activity. The results demonstrated a significant increase in flexion of the digits (P < 0.001) and elbow (P < 0.005), during walking as compared with standing, sitting or laying supine. These results were indicative of an overall excessive activation coupling between the upper and lower extremities after stroke. Indeed both voluntary finger flexion and voluntary leg extension produced significant activity in the other impaired extremity, leg and arm, respectively, in the stroke as compared with the control subjects. Thus, rectus femoris in the impaired leg was active during finger flexion of the impaired hand in the stroke survivors and all four tested muscles in the impaired arm were active during extension of the legs (P < 0.05). These findings suggest an interlimb coupling related to active motor tasks, contributing to an upper extremity flexion bias following stroke.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".