A Standardized Arabic Reading Acuity Chart: The Balsam Alabdulkader‐Leat Chart
Bibliographic record
Abstract
PURPOSE: The aim of this study was to develop and validate the first standardized Arabic continuous text near-visual-acuity chart, the Balsam Alabdulkader-Leat (BAL) chart. METHODS: Three versions of the BAL chart were created from previously validated sentences. Reading acuity (RA) and reading speed in standard-length words per minute (SLWPM) were measured for three versions of the BAL chart and three English charts (MNREAD, Colenbrander, and Radner) for 86 bilingual adults with normal vision aged 15 to 59 years. The RA and SLWPM were compared using analysis of variance. To analyze agreement between the charts, Bland-Altman plots were used. Normal visual acuity (0.00 logMAR [log minimum angle of resolution]) was calibrated for the BAL chart with linear regression analysis. RESULTS: Average RAs for BAL1, BAL2, and BAL3 were 0.62, 0.64 and 0.65 log-point print, respectively, which were statistically significantly different (repeated-measures analysis of variance, P < .05), but not considered clinically significant. The coefficients of agreement for RA between the BAL charts were 0.054 (between 1 and 2), 0.061 (between 2 and 3), and 0.059 (between 1 and 3). Linear regression between the average RA for the BAL chart and the MNREAD and Radner charts showed that 0.7 log-point size at 40 cm is equivalent to 0.00 logMAR, and the new BAL chart was labeled accordingly. Mean SLWPM for the BAL charts was 201, 195, and 195 SLWPM, respectively, and for the Colenbrander, MNREAD, and Radner charts was 146, 171, and 146, respectively. The coefficients of agreement for log-SLWPM between BAL1 and BAL2, BAL2 and BAL3, and BAL1 and BAL3 were 0.063, 0.064, and 0.057 log-SLWPM, respectively. CONCLUSIONS: The BAL chart showed high interchart agreement. It is recommended for accurate near performance measures in Arabic for both research and clinical settings.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".