Added cognitive demand from increased sensorimotor complexity resulted in better goal-directed movement following stroke: a novel finding in post-stroke recovery
Bibliographic record
Abstract
BACKGROUND: Stroke often causes impairments in goal-directed movements, which are commonly assessed using goal-directed reaching tasks. Here we present two individuals post-stroke who performed two robotic reaching tasks: 1. Visually Guided Reaching (VGR); reaching a cursor representing the fingertip to virtual targets, 2. Reverse Visually Guided Reaching (RVGR); where the cursor moves in the opposite direction of hand motion, requiring a novel cognitive rule. Participants are typically more successful at reaching targets in the simple VGR task compared to the more complex RVGR task (~92% of our database). The two cases are notable as they performed better on RVGR compared to VGR. CASE DESCRIPTIONS: Case 1: 80-year-old patient with left hemorrhagic thalamic stroke, with interventricular extension, presenting with right hemiparesis/reduced motor control. They were unable to complete reaches to any targets in VGR (0/40 targets), but were able to reach ~30% of the targets (15/48) in RVGR. Case 2: 76-year-old patient with a left ischemic pontine stroke, presenting with right hemiparesis/reduced motor control. They were unable to complete reaches to any targets in VGR (0/40) but were able to reach ~60% of the targets in RVGR (29/48). INTERPRETATION: Better performance in RVGR compared to VGR may be associated with a “dual-task benefit” and has potential clinical implications, including informing neuro-rehabilitative strategies, potentially by including tasks with added complexity or cognitive components. These findings also highlight the utility of robotic tools to provide novel environments within which to identify unique patterns of impairments and abilities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".