Mild Stroke Affects Pointing Movements Made in Different Frames of Reference
Bibliographic record
Abstract
BACKGROUND: Motor performance is a complex process controlled in task-specific spatial frames of reference (FRs). Movements can be made within the framework of the body (egocentric FR) or external space (exocentric FR). People with stroke have impaired reaching, which may be related to deficits in movement production in different FRs. OBJECTIVE: To characterize rapid motor responses to changes in the number of degrees of freedom for movements made in different FRs and their relationship with sensorimotor and cognitive impairment in individuals with mild chronic stroke. METHODS: Healthy and poststroke individuals moved their hand along the contralateral forearm (egocentric task) and between targets in the peripersonal space (exocentric task) without vision while flexing the trunk. Trunk movement was blocked in randomized trials. RESULTS: For the egocentric task, controls produced the same endpoint trajectories in both conditions (free- and blocked-trunk) by preserving similar shoulder-elbow interjoint coordination (IJC). However, endpoint trajectories were dissimilar because of altered IJC in stroke. For the exocentric task, controls produced the same endpoint trajectories when the trunk was free or blocked by rapidly changing the IJC, whereas this was not the case in stroke. Deficits in exocentric movement after stroke were related to cognitive but not sensorimotor impairment. CONCLUSIONS: Individuals with mild stroke have deficits rapidly responding to changing conditions for complex reaching tasks. This may be related to cognitive deficits and limitations in the regulation of tonic stretch reflex thresholds. Such deficits should be considered in rehabilitation programs encouraging the reintegration of the affected arm into activities of daily living.
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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 | 0.002 |
| 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) | 0.003 | 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".