Eye Movements During Smooth Pursuit Training for Different Hemispatial Neglect Subtypes
Bibliographic record
Abstract
Hemispatial neglect is characterized by the failure to perceive, report, and orient to stimuli on the contralesional side of the environment or body. Neglect is most commonly associated with stroke and occurs more frequently and severely in patients with a lesion in the right hemisphere of the brain. Presentation is heterogenous, and hemispatial neglect can be differentiated based on stimulus-centered neglect (allocentric) and viewer-centered neglect (egocentric). The presence of neglect in stroke patients is associated with longer rehabilitation stays and reduced functional outcome compared to those without neglect. Smooth pursuit eye movement training (SPT) is a promising therapeutic intervention for stroke patients experiencing hemispatial neglect. Treatment involves having patients repeatedly follow moving stimulus patterns by making smooth pursuit eye movements from the ipsilesional side to the neglected side. The present study aims to investigate how eye movement behaviour changes during and following multiple SPT sessions. Participants will include right hemisphere stroke patients with acute neglect symptoms which will be recruited from the Rehabilitation Unit at Kelowna General Hospital. Individuals will be administered two SPT sessions on separate occasions while eye movement behaviour is recorded. This is the first research to investigate the effectiveness of SPT as a treatment for the different subtypes of neglect. Due to attentional differences between these subtypes, it is possible that SPT may only be an effective treatment for one type of neglect but not another. Additionally, this research will build on and extend the previous literature by measuring real-time eye movements while participants perform SPT. Ultimately, this will further our understanding of the attentional differences between the neglect subtypes as well as provide clues as to the specifics of how SPT might be affecting patient eye movement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".