Reliability and validity of the Oxford Visual Perception Screen in sub-acute adult stroke survivors
Bibliographic record
Abstract
Objective: Due to a lack of time-efficient standardized assessments, there is a high risk of unidentified visual perception difficulties in stroke survivors. The Oxford Visual Perception Screen (OxVPS) is a 15-min performance-based screen for visual perception difficulties through tasks like picture naming and face recognition. This study evaluates the inter-rater reliability, convergent, and discriminant validity of OxVPS. Method: In this cross-sectional study, 161 stroke survivors within 8 weeks of their stroke, sufficient understanding of English, ability to concentrate for 15 min, and capacity to consent took part across three UK rehabilitation units. Video-recordings of OxVPS assessments were rated by an independent rater for inter-rater reliability. Convergent validity was assessed by comparing OxVPS scores with the Rivermead Perceptual Assessment Battery (RPAB), a 45–90-min battery of visual perceptual tasks. Discriminant validity compared OxVPS scores with performance on the Blind Montreal Cognitive Assessment (MOCA-B) for cognition and with the Visual Impairment Screening Assessment (VISA) for sensory vision. Results: Inter-rater reliability showed equivalent ratings (N = 107, t(106) = −14.77, p < .001) and mean difference of −0.01 point on a 10-point scale in a Bland–Altman analysis (95% confidence interval [CI]: −0.14 to 0.13). Convergent and discriminant validity demonstrated a high correlation of 0.78 (N = 58, 95% CI: 0.65–0.86) between OxVPS and RPAB, lower correlations of 0.52 with MOCA-B scores (N = 113, 95% CI: 0.37–0.64) and .39 with VISA scores (N = 110, 95% CI: 0.22–0.54). Conclusions: Data indicate good inter-rater reliability and evidence that OxVPS predominantly measures visual perception difficulties (convergent validity) in stroke survivors and less so cognition or sensory vision (discriminant validity).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".