Poor upper limb performance despite the absence of notable upper limb motor impairment in adults with acute stroke – the influence of cognitive deficits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: To assess the impact of cognitive impairment, upper limb apraxia, and spatial neglect on upper limb performance in adults with stroke. METHODS: This prospective cross-sectional study evaluated upper limb performance dependency in adults with acute/early subacute stroke. The Upper Limb (UL)-LIMOS assessed upper limb performance; while upper limb motor impairment was evaluated with the Fugl Meyer Assessment-Upper Extremity (FMA-UE), general cognitive function with the Montreal Cognitive Assessment, spatial neglect with the Catherine Bergego Scale, and upper limb apraxia with the Apraxia Screen of TULIA. RESULTS: We recruited 407 adults with stroke. Minimal or no upper limb motor impairments were present in 270 out of 407 (66.3%) adults, among whom 38.5% still exhibited poor upper limb performance. There were weak to moderate correlations between UL-LIMOS and MoCA (r = .213), spatial neglect (r = -.415), and apraxia (r = .190). General cognition, spatial neglect strongly predicted upper limb performance (R2 = 0.34). CONCLUSION: Almost 40% of adults with acute stroke, who do display minimal upper limb impairments, demonstrate poor performance in upper limb tasks, attributed to impaired general cognition, spatial neglect, and/or, to a lesser extent, upper limb apraxia. Hence, there is need for cognitive-motor therapies to be integrated into early rehabilitation settings to address these challenges effectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it