Relationship Between Visuospatial Neglect and Kinesthetic Deficits After Stroke
Bibliographic record
Abstract
BACKGROUND: After stroke, visuospatial and kinesthetic (sense of limb motion) deficits are common, occurring in approximately 30% and 60% of individuals, respectively. Although both types of deficits affect aspects of spatial processing necessary for daily function, few studies have investigated the relationship between these 2 deficits after stroke. OBJECTIVE: We aimed to characterize the relationship between visuospatial and kinesthetic deficits after stroke using the Behavioral Inattention Test (BIT) and a robotic measure of kinesthetic function. METHODS: Visuospatial attention (using the BIT) and kinesthesia (using robotics) were measured in 158 individuals an average of 18 days after stroke. In the kinesthetic matching task, the robot moved the participant's stroke-affected arm at a preset direction, speed, and magnitude. Participants mirror-matched the robotic movement with the less/unaffected arm as soon as they felt movement in their stroke affected arm. RESULTS: We found that participants with visuospatial inattention (neglect) had impaired kinesthesia 100% of the time, whereas only 59% of participants without neglect were impaired. For those without neglect, we observed that a higher percentage of participants with lower but passing BIT scores displayed impaired kinesthetic behavior (78%) compared with those participants who scored perfect or nearly perfect on the BIT (49%). CONCLUSIONS: The presence of visuospatial neglect after stroke is highly predictive of the presence of kinesthetic deficits. However, the presence of kinesthetic deficits does not necessarily always indicate the presence of visuospatial neglect. Our findings highlight the importance of assessment and treatment of kinesthetic deficits after stroke, especially in patients with visuospatial neglect.
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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 | 0.005 |
| 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 | 0.001 |
| 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".