Upper Limb Obstacle Avoidance Behavior in Individuals With Stroke
Bibliographic record
Abstract
BACKGROUND: Upper limb (UL) poststroke hemiparesis commonly leads to chronic disability. Despite moderate-to-good clinical recovery, many patients with UL hemiparesis still do not fully use their arm in daily tasks. Decreased arm use may be related to deficits in performance of more complex movement than what is usually assessed clinically. OBJECTIVE: To identify differences between poststroke and nondisabled control subjects in making complex corrective movements to avoid an obstacle in the reaching path. METHODS: Subjects rapidly reached for a juice bottle on a refrigerator shelf with their hemiparetic or dominant (controls) arm viewed in a large-screen projected 3D virtual environment. In random trials, a sliding door partially obstructed the reaching path. A successful trial was one in which subjects touched the bottle without their arm or hand hitting the door. RESULTS: Fewer participants with stroke (12%) were successful at a 65% success rate in avoiding the door compared to controls (42%). Subjects with stroke also initiated corrections later (further) in the reaching path (100.7 ± 77.6 mm) compared to controls (51.6 ± 31.0 mm) resulting in a reduced margin of error. While both groups used similar endpoint movement strategies for obstructed reaching, subjects with stroke used less elbow and more trunk movement. Participants who reported being more confident using their hemiparetic arm had higher success rates. CONCLUSION: Arm movement deficits can be identified when complex tasks are evaluated. Deficits in higher-order motor function such as obstacle avoidance behavior may decrease actual arm use in individuals with mild-to-moderate hemiparesis and should be evaluated in routine clinical practice.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".