Refractive Errors and Binocular Anomalies in Primary Schools in Uyoun Aljawa: A Small Urban Town in Saudi Arabia
Bibliographic record
Abstract
AIM: To assess the prevalence of uncorrected refractive error (RE) and binocular vision (BV) anomalies in school-aged children in Uyoun Aljawa, a small urban town in Saudi Arabia. METHODS: This was a cross-sectional study of 417 students (aged 6–13 years old) conducted in two primary schools in Uyoun Aljawa from November 2019 to January 2020. All students underwent comprehensive eye examination that include: distance visual acuity (VA), Non- cycloplegic refraction, ocular alignment assessment with the cover test; Near Point of Convergence (NPC) evaluation, Near stereo-acuity with Titmus-fly Stereotest, and finally, colour vision was screened with Ishihara plates. RESULTS: A total of 417 male schoolchildren (mean age ± SD: 9.2 ± 1.9) were included. In this study, 78 (18.4%) students had reduced vision (VA of ≤6/9) of which only 21 (27%) students had spectacles at the time of the study and 19.2% had uncorrected RE (VA of <6/18 and no corrections). Emmetropia was reported in 80.3% of children where hyperopia was the most common refractive error (8.9%) followed by myopia (7.7%), and simple astigmatism was reported only in 3.1%. Heterophoria was reported in 12.5% of the sample, 5.2% had convergence insufficiency, and 16.3% showed subnormal results in stereo-acuity and 11 cases had a colour vision deficiency. CONCLUSION: The results of this study reveal a high prevalence of RE and other BVA among schoolchildren in Uyoun Aljawa. Vision Screening programs of children for RE and BVA should be conducted at the community level and integrated into school health programmes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".