Assocıations Between Executive Functions, Attentıon Skills and Upper Extremity Motor Abilities in Individuals with Chronic Stroke
Bibliographic record
Abstract
ABSTRACT Introduction: One of the most common problems encountered after stroke is the loss of motor function in the upper extremities. Improvement in upper extremity motor function is very important to minimize long-term limitations and improve quality of life. Objective: This study aims to examine the relationship between executive functions, attention skills and upper extremity motor functions in chronic stroke individuals who are considered cognitively normal. Method: 58 individuals with chronic stroke who were treated at Erenköy Physical Therapy and Rehabilitation Hospital were included in the study. Montreal Cognitive Assessment Scale (MoCA), Stroop Test TBAG Form and Digit Span Test were used to assess the cognitive, executive and attention skills of the individuals. Fugl Meyer Upper Extremity Assessment Scale (FMA-UE) and Box and Block Test (BBT) were used to assess the level of upper extremity functions. Results: When we look at the findings of the study, the upper extremity motor functions of the individuals had a positive correlation with the MoCA and Digit Span Test scores and a negative correlation with the Stroop Test components (p<0.05). Cognitive functions were found to be 40% effective on upper extremity motor functions according to the regression analysis between MoCA and BBT and 17% effective on upper extremity motor functions according to the regression analysis between MoCA and FMA-UE. Conclusion: The results of the study confirm the relationship between executive functions and attention skills and upper extremity motor functions in chronic stroke patients who are considered cognitively normal. Taking executive function and attention components into consideration when creating protocols during the rehabilitation process will help therapists create personalized and successful programs. Keyswords: Stroke, Upper Extremity, Cognitive Fonction, MoCA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".