Web-Based Visual Acuity Testing under Low-Resource Settings
Bibliographic record
Abstract
Conventional eye examination tests are available to diagnose visual acuity; however, most of those cannot be performed in low-resource settings and require more money and technical resources. In this study, a novel web solution was developed to replace the traditional Snellen chart method for visual acuity. Numerous technologies pertinent to optimization under low-resource settings were thoroughly examined and integrated throughout the development process, encompassing web frameworks, database management systems, voice recording algorithms, and user interface design. CodeIgniter was used as the framework of this developed system. After conducting tests with real users and obtaining results, a statistical analysis based on the test results was performed. Thirty-four volunteers participated in detecting a logMAR difference of 0.1 between the manual Snellen chart testing and web-based application, assuming a paired t-test, a standard deviation of 0.2 (estimated from previous studies), 2-sided alpha (α) of 0.05, and 80% power. The observations were independent, and the variables were normally distributed. Confidence interval limits were reported for 95%, with the standard deviation ± 1.96 from the mean, ± 0.2 logMAR. A vital feature of the system is its capability to perform a 10-ft and 20-ft eye test based on distance. The application scales the Snellen characters according to the test distance without affecting accuracy. Other unique features include automatic voice recording, automatic Snellen level change and character scaling, storing previous readings for future evaluations, and comparing them with current readings. The accuracy of the eyes, as well as their errors, are displayed to users according to international standards. Our web application produced results almost identical to those of the manual Snellen procedure, even under low resource settings, when compared to the manual Snellen procedure
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".