Persistent visual perceptual disorders after stroke: Associated factors
Bibliographic record
Abstract
Introduction Visual perceptual disorders are common after stroke and often affect functional independence. Little is known about biopsychosocial variables related to these disorders. This study aimed to identify which variables best explain the persistence of visual perceptual disorders in seniors with stroke. Method Visual perception of 195 people aged 65 and over with stroke was assessed using the Motor-free Visual Perceptual Test – Vertical version, three weeks after returning home as well as three and six months later. Biopsychosocial and personal variables were also assessed three weeks after returning home. Correlation analyses were followed by bivariate linear regression analyses. Results Seventy participants still had visual perceptual disorders six months later. Many variables measured at the first test were found to be associated with visual perception at the six-month follow-up. Those best explaining the persistence of visual perceptual dysfunctions ( R 2 = 49.2%) were memory (Wechsler, delayed recall) ( p < 0.001), verbal comprehension (Token Test) ( p = 0.015), stroke severity (Canadian Neurological Scale) ( p = 0.005) and sex (female) ( p = 0.02). Conclusion Among many variables, four (memory, verbal comprehension, stroke severity and sex) were most strongly associated with persistent visual perceptual disorders. Other studies are needed to better understand the role of memory and verbal comprehension in visual perceptual disorders after stroke.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".