Undermining a common language: smartphone applications for eye emergencies
Bibliographic record
Abstract
BACKGROUND: Emergency room physicians are frequently called upon to assess eye injuries and vision problems in the absence of specialized ophthalmologic equipment. Technological applications that can be used on mobile devices are only now becoming available. OBJECTIVE: To review the literature on the evidence of clinical effectiveness of smartphone applications for visual acuity assessment marketed by two providers (Google Play and iTunes). METHODS: The websites of two mobile technology vendors (iTunes and Google Play) in Canada and Ireland were searched on three separate occasions using the terms "eye", "ocular", "ophthalmology", "optometry", "vision", and "visual assessment" to determine what applications were currently available. Four medical databases (Cochrane, Embase, PubMed, Medline) were subsequently searched with the same terms AND mobile OR smart phone for papers in English published in years 2010-2017. RESULTS: A total of 5,024 Canadian and 2,571 Irish applications were initially identified. After screening, 44 were retained. Twelve relevant articles were identified from the health literature. After screening, only one validation study referred to one of our identified applications, and this one only partially validated the application as being useful for clinical purposes. CONCLUSION: Mobile device applications in their current state are not suitable for emergency room ophthalmologic assessment, because systematic validation is lacking.
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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 | 0.020 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".