Do picture-based charts overestimate visual acuity? Comparison of Kay Pictures, Lea Symbols, HOTV and Keeler logMAR charts with Sloan letters in adults and children
Bibliographic record
Abstract
PURPOSE: Children may be tested with a variety of visual acuity (VA) charts during their ophthalmic care and differences between charts can complicate the interpretation of VA measurements. This study compared VA measurements across four pediatric charts with Sloan letters and identified chart design features that contributed to inter-chart differences in VA. METHODS: VA was determined for right eyes of 25 adults and 17 children (4-9 years of age) using Crowded Kay Pictures, Crowded linear Lea Symbols, Crowded Keeler logMAR, Crowded HOTV and Early Treatment of Diabetic Retinopathy Study (ETDRS) charts in focused and defocused (+1.00 DS optical blur) conditions. In a separate group of 25 adults, we compared the VA from individual Kay Picture optotypes with uncrowded Landolt C VA measurements. RESULTS: Crowded Kay Pictures generated significantly better VA measurements than all other charts in both adults and children (p < 0.001; 0.15 to 0.30 logMAR). No significant differences were found between other charts in adult participants; children achieved significantly poorer VA measurements on the ETDRS chart compared with pediatric acuity tests. All Kay Pictures optotypes produced better VA (p < 0.001), varying from -0.38 ± 0.13 logMAR (apple) to -0.57 ± 0.10 logMAR (duck), than the reference Landolt C task (mean VA -0.19 ± 0.08 logMAR). CONCLUSION: Kay Pictures over-estimated VA in all participants. Variability between Kay Pictures optotypes suggests that shape cues aid in optotype determination. Other pediatric charts offer more comparable VA measures and should be used for children likely to progress to letter charts.
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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 | 0.003 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".