Visual Function, Visual Attention, and Mobility Performance in Low Vision
Bibliographic record
Abstract
PURPOSE: The aim of this study was to determine if useful field of view (UFV) measures help to predict aspects of orientation and mobility in people with visual impairment. The UFV is a composite measure of visual attention, ability to detect objects in the presence of clutter and basic visual functions such as visual field loss and contrast sensitivity. METHODS: Thirty-five participants aged 20 to 80 years with low vision due to a variety of visual disorders took part. Mobility around a partly indoor and exterior real-life mobility course was measured, together with UFV and clinical measures of contrast sensitivity (CS), visual fields, and visual acuity. Two series of models were considered; series 1 using the UFV scores as measured and series 2 using the UFV scores corrected for visual field loss (only counting errors in areas of intact visual field). RESULTS: UFV was found to be an important predictor of some aspects of mobility performance. Mobility errors were best predicted by uncorrected UFV (R = 0.38), although CS was also a good predictor. Walking speed and preferred walking speed (PWS) were best predicted by uncorrected UFV and age (R = 0.575 and 0.573, respectively). The visual detection distance and visual identification distances were best predicted by clinical vision measures, such as contrast sensitivity, visual fields, and central vision function. The percent PWS was not predicted by any of the measures we used. None of these models was improved by the addition of the corrected UFV scores. CONCLUSIONS: These results indicate that attention and the presence of distractors, as well as visual function and age, are important factors in orientation and mobility performance, in particular mobility errors, walking speed and PWS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".