Arabic Reading Performance With a Chromatic Acuity Chart
Bibliographic record
Abstract
Purpose: This study compared the reading performance for Arabic text defined by chromatic and achromatic contrast to understand better how chromatic contrast affects reading of normally sighted individuals and to establish a baseline for determining whether patients have a selective red-green chromatic sensitivity loss. Method: Reading performance for Arabic text was accessed by examining maximum reading speed (MRS), reading acuity (RA), critical print size (CPS), and the Reading Accessibility Index (ACC) using three near-point charts. The charts were the black-on-white Balsam Alabdulkader-Leat (BAL) chart, a red-on-green chart, and a gray-on-gray chart with a background luminance equal to the chromatic chart. Results: The MRSs were significantly different (P = 0.03), with the red-on-green chart having a slightly higher value than the BAL chart. The ACC was lower for the BAL chart than the red-on-green and gray charts (P = 0.003). However, RA for the BAL chart was better, and the CPS was smaller relative to the red-on-green chart (P < 0.05) and gray chart (P < 0.001). Individuals with red-green color vision deficiencies had poorer RA and larger CPS on the red-on-green chart relative to the achromatic charts. Conclusions: Although the MRS and ACC of the chromatic chart were significantly higher, the difference was not clinically important. The result that the MRS was similar for all three charts confirmed earlier findings that MRS is similar if text contrast is sufficiently above threshold. The lower RA and corresponding larger CPS for the red-on-green and gray charts were due to their lower background luminance and lower contrast.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".