Myopia Management in Hong Kong
Bibliographic record
Abstract
Objectives: We aimed to investigate how optometrists in Hong Kong are managing myopic and “pre-myopic” children. Methods: Clinical files for children aged 6 to 10 years old who had eye examinations from 2017 to 2021 were retrospectively reviewed. Children were grouped by the initial spherical equivalent refractive error (SER) as myopes or pre-myopes. The demographic data, refractive error, and myopia management recommended by the optometrists were analyzed. Results: A total of 1,318 children (859 myopes and 459 pre-myopes) from ten clinics in Hong Kong were included. Over 5 years, myopia management recommendations shifted significantly (p < 0.001). In 2017, only 18.4% of children were recommended to pursue myopia control (MC), increasing to 42.8% by 2021. The use of MC spectacle lenses increased from 7.3% in 2017 to 36.8% in 2021, becoming the most recommended option. Orthokeratology, MC contact lenses, and atropine remained stable at less than 5% over this period. Children recommended for MC approaches had significantly more myopia than those recommended single-vision lenses or monitoring (p < 0.05). Age of the first visit significantly correlated with SER change from the first visit to the next recommendation update for pre-myopes (r = 0.27, p = 0.013) but not for myopes. Conclusions: From 2017 to 2021, myopia management patterns in Hong Kong shifted significantly, with more children being recommended for myopia control. MC spectacle lenses emerged as the most commonly recommended method. Younger pre-myopes at their first visit were more likely to have earlier management updates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".