Prevalence and Causes of Blindness, Low Vision and Status of Cataract in 50 Years and Older Citizen of Qatar—A Community Based Survey
Bibliographic record
Abstract
BACKGROUND: Rapid Assessment for the Avoidable Blindness (RAAB) was conducted in Qatar during 2009. We present the prevalence and determinants of visual disabilities and status of cataract among citizens aged 50 years and older. METHODS: Residents of randomly selected houses and clusters participated in the survey. Opticians noted the presenting and the best corrected vision of participants from 49 clusters. Ophthalmologists examined participants with additional instruments like bio-microscope, digital camera, auto-perimeter and auto-refractor in a mobile van. World Health Organization recommended principal cause of blindness (Visual acuity [VA] < 3/60 in better eye), Severe visual impairment (SVI) (<6/60), low vision (VA < 6/18) and unilateral blindness (VA < 3/60) were designated. Persons with VA < 6/18 and cataract were interviewed to calculate coverage and barriers for cataract surgeries. Age sex adjusted prevalence of visual disabilities and their 95% Confidence Intervals (CI) were estimated. RESULTS: We examined 2,433 (97.3%) participants. The age sex adjusted prevalence of bilateral blindness was 1.28% [95% CI 1.22-1.35], SVI (1.67%), low vision (3.66%) and unilateral blindness (3.61%) in 50 years and older population. Female and older age groups were significant risk factors of visual disabilities. Cataract and glaucoma were the main causes of visual disabilities. The coverage of cataract services was 68.2%. Believing that cataract as an aging process (25) and adequate vision in the fellow eye (15) were the reasons for delay in surgery. CONCLUSIONS: To reduce avoidable blindness, un-operated cataract should be addressed. Primary and secondary eye care systems should be strengthened to improve the care of blinding eye diseases in Qatar.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".