Distribution and Predictors of Initial Glaucoma Care Among Ophthalmologists and Optometrists: A Population-based Study
Bibliographic record
Abstract
PURPOSE: To evaluate evolution in the distribution of new glaucoma patients between ophthalmologists and optometrists, and to examine factors predicting provider type, in the context of expansion in the scope of optometry practice. PATIENTS AND METHODS: A population-based study was undertaken using validated datasets in Ontario, Canada from 2007 to 2018, encompassing time before and after optometry practice scope expansion in 2011. All patients aged 66 and older receiving a glaucoma suspect diagnosis or first-line therapy for glaucoma from ophthalmologists or optometrists were enrolled. Predictors of provider type were evaluated using logistic regression. RESULTS: From 2007 to 2018, 401,560 patients received initial glaucoma care, including 303,440 by ophthalmologists and 98,120 by optometrists. Population rates of glaucoma suspect diagnosis increased for both providers over the study period. The rate of therapy initiation increased annually among optometrists after 2011, while the rate remained stable over that period among ophthalmologists. By 2018, 88% of patients initiating therapy and 59% of patients first diagnosed as a glaucoma suspect received that care from ophthalmologists. In the final study year, therapy initiations per provider were lower among optometrists (median: 2/provider; interquartile range: 1 to 3) than among ophthalmologists (median: 26.5/provider, interquartile range: 10 to 53). Patients were more likely to receive care from an ophthalmologist than an optometrist if they were older, had higher ocular or systemic comorbidity, or lived in urban settings. CONCLUSIONS: Optometrists have a large and growing role in diagnosing glaucoma suspects; however, despite scope expansion, optometrists play a much smaller role in initiating glaucoma therapy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".