Saturated Muscle Activation Contributes to Compensatory Reaching Strategies After Stroke
Bibliographic record
Abstract
The control and execution of movement could potentially be altered by the presence of stroke-induced weakness if muscles are incapable of generating sufficient power. The purpose of this study was to identify compensatory strategies during a forward (sagittal) reaching task for 20 persons with chronic stroke and 10 healthy age-matched controls. We hypothesized that the paretic anterior deltoid would be maximally activated (i.e., saturated) during a reaching task and that task completion would require activation of additional muscles, resulting in compensatory movements out of the sagittal plane. For reaching movements by control subjects, joint motion remained largely in the sagittal plane and hand trajectories were smooth and direct. Movement characteristics of the nonparetic arm of stroke subjects were similar to control subjects except for small increases in the abduction angle and the percentage that anterior deltoid was activated. In contrast, reaching movements of the paretic arm of stroke subjects were characterized by increased activation of all muscles, especially the lateral deltoid, in addition to the anterior deltoid, with resulting shoulder abduction power and segmented and indirect hand motion. For the paretic arm of stroke subjects, muscle and kinetic compensations increased with impairment severity and weaker muscles were used at a higher percentage of their available muscle activity. These results suggest that the inability to generate sufficient force with the typical agonists involved during a forward reaching task may necessitate compensatory muscle recruitment strategies to complete the task.
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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) | 0.002 | 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".