Myopia Management in Ontario, Canada
Bibliographic record
Abstract
Objectives: To determine how optometrists in Canada manage their pediatric myopia patients and to assess whether this has changed over time. Methods: In a retrospective chart review, records for children aged 6–10 years who had an eye exam between 2017 to 2021 were reviewed. Children were grouped by presenting refraction (myopes ≤ −0.50 D or pre-myopes ≤ +0.75 D). Up to five unique patients were selected for each age (6, 7, 8, 9, and 10) and initial visit year (2017 to 2021) for each group (myopes and pre-myopes), for a maximum of 250 files per practice. Demographic information, refraction, and recommended interventions were recorded. Logistic regression was used to model the likelihood of being prescribed a myopia control intervention based on patient and optometrist characteristics. Results: A total of 2905 patients (n = 1467 (50%) female) from 15 practices across Ontario, Canada, were included, accounting for 8546 visits. Optometrists predominantly prescribed single-vision spectacle correction as a first-line intervention for myopic children, although this declined from 98.2% in 2017 to 56.7% in 2023. The use of myopia control modalities increased from 1.8% to 43.3% over this same period. Optometrists began recommending myopia control at lower myopic refractive errors over time (−2.63 DS in 2017 vs and −1.49 DS in 2020). Myopia control spectacles were the most commonly prescribed intervention, despite the observation that optometrists are not hesitant to fit contact lenses in younger children. Optometrists who had been in practice longer were more likely to prescribe older forms of myopia control (e.g., bifocals/progressives) than more recent graduates. Conclusions: While single-vision spectacle correction remains a primary approach for initial myopia management in Ontario, Canada, optometrists increasingly recommend myopia control and are initiating interventions earlier.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".