Visual function, digital behavior and the vision performance index
Bibliographic record
Abstract
Sarah Farukhi Ahmed,1 Kyle C McDermott,2 Wesley K Burge,2 I Ike K Ahmed,3,4 Devesh K Varma,3 Y Joyce Liao,5 Alan S Crandall,4 S Khizer R Khaderi2,61Shiley Eye Institute of Ophthalmology, University of California, San Diego, CA, USA; 2Vizzario, Inc., Venice, CA, USA; 3Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada; 4Ophthalmology and Visual Sciences, University of Utah School of Medicine, Salt Lake City, UT, USA; 5Ophthalmology and Neurology, Stanford University Medical Center, Stanford, CA, USA; 6Ophthalmology, Stanford University Medical Center, Stanford, CA, USAAbstract: Historically, visual acuity has been the benchmark for visual function. It is used to measure therapeutic outcomes for vision-related services, products and interventions. Quantitative measurement of suboptimal visual acuity can potentially be corrected optically with proper refraction in some cases, but in many cases of reduced vision there is something else more serious that can potentially impact other aspects of visual function such as contrast sensitivity, color discrimination, peripheral field of view and higher-order visual processing. The measurement of visual acuity typically requires stimuli subject to some degree of standardization or calibration and has thus often been limited to clinical settings. However, we are spending increasing amounts of time interacting with devices that present high-resolution, full color images and video (hereafter, digital media) and can record our responses. Most of these devices can be used to measure visual acuity and other aspects of visual function, not just with targeted testing experiences but from typical device interactions. There is growing evidence that prolonged exposure to digital media can lead to various vision-related issues (eg, computer vision syndrome, dry eye, etc.). Our regular, daily interactions (digital behavior) can also be used to assess our visual function, passively and continuously. This allows us to expand vision health assessment beyond the clinic, to collect vision-related data in the whole range of settings for typical digital behavior from practically any population(s) of interest and to further explore just how our increasingly virtual interactions are affecting our vision. We present a tool that can be easily integrated into digital media to provide insights into our digital behavior.Keywords: vision education, visual acuity, vision assessment, technology, video games, mobile gaming, software applications, mobile applications
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".