Development of the Halifax Visual Scanning Test: A New Measure of Visual-Spatial Neglect for Personal, Peripersonal, and Extrapersonal Space
Bibliographic record
Abstract
OBJECTIVES: Visual-spatial neglect is a common attentional disorder after right-hemisphere stroke and is associated with poor rehabilitation outcomes. The presence of neglect symptoms has been reported to vary across personal, peripersonal, and extrapersonal space. Currently, no measure is available to assess neglect severity equally across these spatial regions and may be missing subsets of symptoms or patients with neglect entirely. We sought to provide initial construct validity for a novel assessment tool that measures neglect symptoms equally for these spatial regions: the Halifax Visual Scanning Test (HVST). METHODS: In Study I, the HVST was compared to conventional measures of neglect and functional outcome scores (wheelchair navigation) in 15 stroke inpatients and 14 healthy controls. In Study II, 19 additional controls were combined with the control data from Study I to establish cutoffs for impairment. Patterns of neglect in the stroke group were examined. RESULTS: In Study I, performance on all HVST subtests were correlated with the majority of conventional subtests and wheelchair navigation outcomes. In Study II, neglect-related deficits in visual scanning showed dissociations across spatial regions. Four inpatients exhibited symptoms of neglect on the HVST that were not detected on conventional measures, one of which showed symptoms in personal and extrapersonal space exclusively. CONCLUSIONS: The HVST appears a useful measure of neglect symptoms in different spatial regions that may not be detected with conventional measures and that correlates with functional wheelchair performance. Preliminary control data are presented and further research to add to this normative database appears warranted.
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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) | 0.001 | 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 | 0.001 |
| 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".