Allocentric Versus Egocentric Neglect in Stroke Patients: A Pilot Study Investigating the Assessment of Neglect Subtypes and Their Impacts on Functional Outcome Using Eye Tracking
Bibliographic record
Abstract
OBJECTIVE: Few studies have investigated the assessment and functional impact of egocentric and allocentric neglect among stroke patients. This pilot study aimed to determine (1) whether allocentric and egocentric neglect could be dissociated among a sample of stroke patients using eye tracking; (2) the specific patterns of attention associated with each subtype; and (3) the nature of the relationship between neglect subtype and functional outcome. METHOD: Twenty acute stroke patients were administered neuropsychological assessment batteries, a pencil-and-paper Apples Test to measure neglect subtype, and an adaptation of the Apples Test with an eye tracking measure. To test clinical discriminability, twenty age- and education-matched control participants were administered the eye tracking measure of neglect. RESULTS: The eye tracking measure identified a greater number of individuals as having egocentric and/or allocentric neglect than the pencil-and-paper Apples Test. Classification of neglect subtype based on eye tracking performance was a significant predictor of functional outcome beyond that accounted for by the neuropsychological test performance and Apples Test neglect classification. Preliminary evidence suggests that patients with no neglect symptoms had superior functional outcomes compared with patients with neglect. Patients with combined egocentric and allocentric neglect had poorer functional outcomes than those with either subtype. Functional outcomes of patients with either allocentric or egocentric neglect did not differ significantly. The applications of our findings, to improve neglect detection, are discussed. CONCLUSION: Results highlight the potential clinical utility of eye tracking for the assessment and identification of neglect subtype among stroke patients to predict functional outcomes. (JINS, 2019, 25, 479-489).
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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 | 0.000 |
| 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".