Correlation of Fatigue with Cognition and Motor Performance among Stroke Patients
Bibliographic record
Abstract
Introduction: Post-Stroke Fatigue (PSF) is the most common debilitating and persistent symptom after a stroke.The impact of fatigue increases during the first year of post-stroke. When most of the recovery has taken place,fatigue could remain an important problem with disabling consequences for everyday life. Understanding theeffect of PSF on cognition and mobility outcomes will help to better manage existing fatigue symptoms in strokepatients and develop strategies to optimize mobility and cognitive performance in patients with the chronic stageof stroke. With this background in mind, the present study aims at establishing a relationship between post-strokefatigue and cognitive and motor performance among persons with stroke.Aim: To correlate fatigue with cognition and motor performance in stroke patientsMaterials and Method: This is the pilot study where 15 post-stroke patients fulfilling the inclusion and exclusioncriteria were included by using the purposive sampling technique. Fatigue was assessed using Fatigue Scale forMotor and Cognitive Function. Motor Function was assessed by using Fugl Meyer Scale for upper and lowerextremity, Chedokes Arm and Hand Inventory, Lower Extremity Functional Scale, and Berg Balance Scale.Cognition was assessed by using the Montreal Cognitive Assessment Scale. Using the scores of these scales, fatiguewas correlated with motor and cognitive functions using Karl Pearson’s correlation test.Result: The study found a moderately significant correlation between Fatigue and Cognition (r=-0.605 p<0.05) anda highly significant correlation between fatigue and motor performance (r=-0.804 p<0.001).Conclusion: This study provides evidence that post-stroke fatigue has significant relationships with both motorperformance and cognitive performance. It is important to consider the influence of fatigue when planning anddelivering interventions for individuals with stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".