Reading with normal vision and with age-related macular degeneration
Bibliographic record
Abstract
Purpose: Patients with age-related macular degeneration (AMD) may read with their peripheral retina. Due to crowding effects and poor ocular motor control, these patients may benefit from larger spacing between the letters and from serif type-fonts. In this research we tested the influence of four common type-fonts on reading performance in people with normal vision and in patients with AMD. Method: Four versions of the MNRead charts were tested on 24 people with normal vision and 19 patients with AMD. The charts were printed using common type-fonts: 1) Times New Roman (proportional spaced, serif), 2) Courier (mono-spaced, serif), 3) Arial (proportional spaced, sans serif), and 4) Andale Mono (mono-spaced, sans serif). Binocular visual acuity was measured with ETDRS. Results: People with normal vision read best on the Andale Mono chart. On this chart, the largest proportion of people (83%) read the full sentence at the smallest print size (20/13). They also had the best reading acuity (−0.17 ± 0.05 logMAR), critical print size (0.05 ± 0.11 logMAR), and maximum reading speed (233.06 ± 41.69 wpm). However, on the Times New Roman chart, people with normal vision performed worst in all measures. Patients with AMD read more lines on the Courier chart than on any other charts. On this chart, these patients yielded the best reading acuity (0.56 ± 0.17 logMAR), critical print size (0.70 ± 0.20 logMAR), and second largest maximum reading speed (104.22 ± 61.43 wpm). Patients read fastest on Andale Mono charts (107.12 ± 56.57 wpm). In contrast, on the Arial chart, patients with AMD did the worst. Conclusion: Reading performance of people with normal vision is best on a mono-spaced sans-serif font, while that of patients with AMD is better on a type-font that is mono-spaced and serif.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".