Prevalence of uncorrected refractive error and other eye problems among urban and rural school children
Bibliographic record
Abstract
BACKGROUND: Uncorrected refractive error is an avoidable cause of visual impairment. AIM: To compare the magnitude and determinants of uncorrected refractive error, such as age, sex, family history of refractive error and use of spectacles among school children 6-15 years old in urban and rural Maharashtra, India. STUDY DESIGN: This was a review of school-based vision screening conducted in 2004-2005. MATERIALS AND METHODS: Optometrists assessed visual acuity, amblyopia and strabismus in rural children. Teachers assessed visual acuity and then optometrists confirmed their findings in urban schools. Ophthalmologists screened for ocular pathology. Data of uncorrected refractive error, amblyopia, strabismus and blinding eye diseases was analyzed to compare the prevalence and risk factors among children of rural and urban areas. RESULTS: We examined 5,021 children of 8 urban clusters and 7,401 children of 28 rural clusters. The cluster-weighted prevalence of uncorrected refractive error in urban and rural children was 5.46% (95% CI, 5.44-5.48) and 2.63% (95% CI, 2.62-2.64), respectively. The prevalence of myopia, hypermetropia and astigmatism in urban children was 3.16%, 1.06% and 0.16%, respectively. In rural children, the prevalence of myopia, hypermetropia and astigmatism was 1.45%, 0.39% and 0.21%, respectively. The prevalence of amblyopia was 0.8% in urban and 0.2% in rural children. Thirteen to 15 years old children attending urban schools were most likely to have uncorrected myopia. CONCLUSION: The prevalence of uncorrected refractive error, especially myopia, was higher in urban children. Causes of higher prevalence and barriers to refractive error correction services should be identified and addressed. Eye screening of school children is recommended. However, the approach used may be different for urban and rural school children.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".