Bibliographic record
Abstract
Myopia is an enormous, and growing, public health issue across the globe. The prevalence of myopia has doubled in just the past 50 years and it is estimated that approximately half of the world’s population (4.8 billion people) will be affected by 2050. The increase has been especially pronounced in individuals of East Asian descent, where 80-90% of young adults are now myopic. Myopia is now the most common cause of visual impairment and the second most common cause of blindness worldwide. While often considered a “correctable” cause of vision loss, people with myopia have an increased lifetime risk of complications, such as macular degeneration and retinal detachment, which can cause long-term visual impairment or even blindness. Although all levels of myopia are associated with an increased risk of complications, the risk is substantially greater in people with high myopia (defined by the World Health Organization as a refractive error of ≤-5 diopters ). In addition to a large burden of visual impairment, myopia also has a significant global economic cost, estimated to be $250 billion per year in lost productivity, which is almost certain to rise. With these factors in mind, preventing the progression of myopia is a global public health priority. The purpose of this article is to review the currently available methods to treat myopia progression in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".