The Status of Childhood Blindness and Functional Low Vision in the Eastern Mediterranean Region in 2012
Bibliographic record
Abstract
Childhood blindness and visual impairment (CBVI) are major disabilities that compromise the normal development of children. Health resources and practices to prevent CBVI are suboptimal in most countries in the Eastern Mediterranean Region (EMR). We reviewed the magnitude and the etiologies of childhood visual disabilities based on the estimates using socioeconomic proxy indicators such as gross domestic product (GDP) per capita and <5-year mortality rates. The result of these findings will facilitate novel concepts in addressing and developing services to effectively reduce CBVI in this region. The current study determined the rates of bilateral blindness (defined as Best corrected visual acuity(BCVA)) less than 3/60 in the better eye or a visual field of 10° surrounding central fixation) and functional low vision (FLV) (visual impairment for which no treatment or refractive correction can improve the vision up to >6/18 in a better eye) in children <15 years old. We used the 2011 population projections, <5-year mortality rates and GDP per capita of 23 countries (collectively grouped as EMR). Based on the GDP, we divided the countries into three groups; high, middle- and low-income nations. By applying the bilateral blindness and FLV rates to high, middle- and low-income countries from the global literature to the population of children <15 years, we estimated that there could be 238,500 children with bilateral blindness (rate 1.2/1,000) in the region. In addition, there could be approximately 417,725 children with FLV (rate of 2.1/1,000) in the region. The causes of visual disability in the three groups are also discussed based on the available data. As our estimates are based on hospital and blind school studies in the past, they could have serious limitations for projecting the present magnitude and causes of visual disabilities in children of EMR. An effective approach to eye health care and screening for children within primary health care and with the available resources are discussed. The objectives, strategies, and operating procedures for child eye-care are presented. Variables impacting proper screening are discussed. To reach the targets, we recommend urgent implementation of new approaches to low vision and rehabilitation of 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".