Bibliographic record
Abstract
The global burden of myopia represents a significant public health concern that is expected to continue to increase in the near future. It is estimated that 50% of the world’s population will be affected by myopia by 2050, with a disproportionately high prevalence in Asia. High myopia, where the spherical equivalent refractive error is equal to or higher than 6.00 diopters, is expected to increase in prevalence from 2.7% to 10% during this period. The severity of myopia is of paramount concern to clinicians as higher levels are associated with pathologic myopia (PM) and increased risk of vision loss. Pathologic myopia, as recently defined by the International Myopia Institute, is an excessive axial elongation associated with myopia that leads to structural changes in the posterior segment of the eye that can lead to loss of best-corrected visual acuity. These structural changes and their complications include posterior staphyloma, myopic choroidal neovascularization, myopic maculopathy, myopic traction maculopathy, dome shaped maculopathy, optic disc changes and glaucoma associated with myopia, and retinal detachments.
 The advent of optical coherence tomography (OCT) has facilitated the characterization, diagnosis, and management of several of these complications associated with PM and will be the focus of this article. Imaging the highly myopic eye represents a crucial step in the identification of these complications and poses its own unique challenges. Researchers have demonstrated the advantage of 3D cube scans in the detection of pathology compared to 1- and 5-line rasters. Using vertical scanning patterns aligning where the radius of curvature is larger relative to the horizontal plane of the myopic eye can minimize associated artifacts. Wide scans, facilitated by emerging technologies such as swept- source OCT and ultra wide-field OCT, are useful in cases of PM where the pathology can initiate peripherally.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".