School-based programme to address childhood myopia in Singapore
Bibliographic record
Abstract
In 2050, a total of 4,758 million people worldwide (49.8% of the world’s population) are expected to be myopic, and 938 million people (9.8%) are expected to suffer from high myopia (myopia worse than −5.00 D).(1) Myopia, commonly known as short-sightedness or near-sightedness, has remained one of the biggest public health challenges in Singapore, which has very high myopia rates. Myopia typically begins in early childhood and progresses during childhood. Based on unpublished data from routine vision screening done by the School Health Service (SHS) using the Snellen chart, the prevalence of defective vision amounting to unaided visual acuity of 6/12 or worse among Primary 1 students was 33% in the year 2000, prior to the inception of the National Myopia Prevention Programme (NMPP). 65% of Singapore students suffered from myopia by the age of 12 years. Among these 12-year-old students, the prevalence of severe defective vision (i.e. unaided visual acuity worse than 6/60) was 13% (unpublished data). Age of onset of myopia and duration of myopia progression are the most important predictors of high myopia in later childhood.(2) Children with high myopia in the range of ≤ −5.0 D to −10.0 D(3) have higher risks of complications, such as myopic macular degeneration,(4-6) retinal detachment,(7) glaucoma(8) and blindness.(9) There was concern that if such trends continued, more than 80% of the population would be suffering from myopia by adulthood, and among them, a high proportion would have high myopia.(10-13) Given that Singapore has an ageing population, this would increase the burden on the healthcare system because of the greater need to provide treatment for myopia-related complications. As about 80% of male enlistees are myopic at the time of enlistment for National Service in Singapore, adjustments have to be made to accommodate the occupational demands of military duties, incurring additional costs.(11,14) The pool of candidates for National Service duties and military occupations that require good vision, such as pilots, would also be more limited.(11,14)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".