Reliability and Validity of the Oxford Visual Perception Screen in Sub-Acute Adult Stroke Survivors
Bibliographic record
Abstract
Due to a lack of time-efficient standardised assessments, there is a high risk of unidentified visual perception difficulties in stroke survivors. The Oxford Visual Perception Screen (OxVPS) is a 15-minute performance-based screen for visual perception difficulties through tasks like picture naming and face recognition. In this cross-sectional study, the inter-rater reliability, convergent and discriminant validity of the OxVPS was evaluated in 161 stroke survivors in three UK rehabilitation units. Inclusion criteria were stroke survivors aged 18 years or over with sufficient understanding of English, ability to concentrate for 15-minutes, and capacity to consent. 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-minute 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. 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]: -0.14-0.13). Convergent and discriminant validity demonstrated a high correlation of .78 (N=58, 95% CI: .65-.86) between OxVPS and RPAB, lower correlations of .52 with MOCA-B scores (N=113, 95% CI: .37-.64) and .39 with VISA scores (N=110, 95% CI: .22-.54). Data indicates 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".