Validating a novel visual field assessment app: A pilot study
Bibliographic record
Abstract
Introduction The paper Cullen chart has been a validated adjunct to perimeters in detecting scotomas for various neuro-ophthalmological pathologies for decades. It was digitized into a prototype-app to empower future users to conduct remote monitoring of visual fields. This project aimed to refine the apps' usability for future users to self-assess and monitor their visual fields by exploring the difficulties faced using the app, to gather feedback, and subsequently to improve its usability for future iterations to objectively compare iterations using the MAUQ scores. Methods Participants (n = 15; age: 24-58) recruited through convenience sampling underwent mixed (quantitative and qualitative) methods to measure 1. Participants' adherence to the app instruction through observation, 2. objective experiences of using the app through self-reporting using the mHealth App Usability Questionnaire (MAUQ), and 3. Subjective experience of app using through semi-structured interviews. Descriptive analysis was computed for observation and MAUQ data. Thematic analysis was adopted to analyse the semi-structured interview data. Results 1/15 adhered to 3 written instructions and 8/15 participants had awkward hand movements. The MAUQ median score was 123/147, the MAUQ domain mean scores - ease of use and satisfaction, system information arrangement and usefulness were 81.6%(45.7/56), 80.6%(33.9/42) and 80.2%(39.3/49), respectively. Questions 4, 5, 9, 11 and 19 were the 5 lowest-scoring questions. Qualitative data were categorised into instructions, test, and feedback which had codes and subcodes. Conclusion Feedback for improvements were surrounding central fixation, remembering peripheral stimuli, uncover eye when interacting with peripheral stimuli, video examples, an introduction to the app and audio instructions.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".