Psychometric Validation of the ViSIO-PRO and ViSIO-ObsRO in Retinitis Pigmentosa and Leber Congenital Amaurosis
Bibliographic record
Abstract
INTRODUCTION: Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis (LCA) are rare inherited retinal degenerative disorders. The Visual Symptom and Impact Outcomes patient-reported outcome (ViSIO-PRO) and observer-reported outcome (ViSIO-ObsRO) instruments were developed in this population to assess visual function symptoms and impacts on vision-dependent activities of daily living (ADL) and distal health-related quality of life (HRQoL). This study aimed to explore the psychometric properties of the ViSIO-PRO and ViSIO-ObsRO in RP/LCA. METHODS: The 49-item ViSIO-PRO and 27-item ViSIO-ObsRO instruments were completed by 83 adult and adolescent patients and 22 caregivers of child patients aged 3-11 years with RP/LCA, respectively, at baseline and 12-16-day follow-up. Concurrent measures were also administered at baseline. Psychometric analyses assessed item (question) properties, dimensionality, scoring, reliability, validity, and score interpretation. RESULTS: Item responses were mainly evenly distributed across the response scale, and inter-item correlations were mostly moderate to strong (> 0.30) at baseline within hypothesized domains. Item deletion was informed by item properties, qualitative data, and clinical input and supported retention of 35 ViSIO-PRO items and 25 ViSIO-ObsRO items. Confirmatory factor analysis in line with pre-hypothesized domains supported a four-factor model assessing visual function symptoms, mobility, vision-dependent ADL, and distal HRQoL. A bifactor model supported calculation of total scores and four domain scores. Internal consistency was high for domain and total scores (Cronbach's alpha > 0.70) and test-retest reliability for total scores was strong between baseline and 12-16-day follow-up (intraclass correlation coefficients 0.66-0.98). Convergent validity was supported by strong correlations in a logical pattern with concurrent measures. Mean baseline scores differed significantly between severity groups. Distribution-based methods provided initial insights to guide interpretation of scores. CONCLUSIONS: Findings supported item reduction and established scoring of the instruments. Evidence of reliability and validity as outcome measures in RP/LCA was also reported. Further research is ongoing to explore responsiveness of the ViSIO-PRO and ViSIO-ObsRO instruments and interpretation of change scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".