Upper Extremity-Cognitive Dual-Task Capacity Post-Stroke
Bibliographic record
Abstract
Background Dual-task capacity, which might be impaired poststroke, is needed for daily functions. Therefore, dual-task capacity should be assessed during rehabilitation. The Dual Overload Interference Test (DO-IT) is a new upper extremity (UE) protocol for assessment, combining The Box and Block Test with the Counting Backwards Test. Objectives To validate DO-IT by comparing between (1) young and older healthy, (2) stroke and healthy participants. Additionally, to correlate DO-IT with (3) walking-cognitive dual-task assessment (healthy), and (4) standardized cognitive and EF assessments (stroke). Methods A cross-sectional study included younger and older community-dwelling healthy individuals (N = 32), and younger and older individuals with stroke (N = 83). DO-IT was administered to all participants. The #blocks transferred (motor) and #correct numbers counted (cognitive) were recorded for single and dual conditions. The walking-cognitive dual-task test was administered to the healthy participants. Motor and cognitive costs were calculated as the difference between single and dual tasks. The Montreal Cognitive Assessment test (MoCA) and the Color Trail Test (CTT) assessed cognition post stroke. Results Older healthy adults had significantly lower dual-task motor capacity compared to younger adults (median [interquartile range] blocks: older 26 [21-38], younger 46 [38-52], P < .01). Participants with stroke showed higher motor costs than healthy participants. Dual-task costs correlated between DO-IT to walking-cognitive (motor; r = .37-.41, cognitive; r = .41-.47, P < .05). DO-IT motor cost negatively correlated with MoCA ( r = −.27, P < .05), and DO-IT motor performance correlated with MoCA/CTT-A\B ( r = .29-.60, P < .05). Conclusions UE-Cognitive dual-task capacity is affected post-stroke. DO-IT shows potential for use in stroke rehabilitation and its validity should be further researched.
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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) | 0.001 | 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) | 0.004 | 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".