Poor upper limb performance despite the absence of notable upper limb motor impairment in adults with acute stroke – the influence of cognitive deficits
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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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".