Spatial Neglect Predicts Upper Limb Use in the Activities of Daily Living
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Motor tests performed at stroke onset have been shown to predict the recovery of upper limb motor impairment. Less is known about upper limb recovery at the level of functional activity or of participation and how spatial neglect may influence the integration of the upper limb in the activities of daily living (ADL). Our objective was to investigate whether the initial severity of spatial neglect may predict upper limb use in ADL. METHODS: Eighty-two patients with a right-hemispheric stroke (RHS) were prospectively included in the study. They were assessed twice in the acute/subacute and in the subacute/chronic phases (mean time interval of 45 days) after stroke. The Catherine Bergego Scale (CBS) was used to quantify the influence of spatial neglect on the ADL. Contralesional upper limb use in the ADL was evaluated with the Lucerne international classification of function, disability and health-based Multidisciplinary Observation Scale. Hand strength was measured using the Jamar, dexterity with the Nine Hole Peg test, and tactile perception using the stereognosis subtest of the Nottingham Sensory Assessment. Cognitive functions were assessed with the Montreal Cognitive Assessment. RESULTS: Regression analyses revealed that spatial neglect is an independent and a significant predictor of upper limb outcome. A CBS score of ≤5 at the time of admission to neurorehabilitation care was highly predictive for good upper limb use in the ADL 45 days later. CONCLUSIONS: This study demonstrates that spatial neglect severity, as observed in the ADL, is a significant and an independent predictor of upper limb outcome. Neglect therapy is thus needed to further improve contralesional upper limb use in the ADL in RHS patients.
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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 | 0.000 |
| 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) | 0.003 | 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".