A Cross-Sectional Survey of Optometrists in Canada Regarding Referral Patterns and a Needs Assessment for an Artificial Intelligence Referral Screening Tool for Epiretinal Membrane
Bibliographic record
Abstract
Background and Objective This study evaluated optometrists' referral patterns for epiretinal membrane (ERM) patients in Ontario, Canada, and their attitudes towards an artificial intelligence (AI) tool for improving referral accuracy. An anonymous online survey with 11 questions was conducted. Patients and Methods The survey targeted optometrists across Ontario, Canada. The survey aimed to understand optometrists' reasons for referring ERM patients to retina specialists, their expectations of the specialists' management, and their openness to using an AI tool for triage. To prevent bias, the survey described an AI tool as an online consultation feature limited to predefined questions without directly mentioning “AI.” The main objective was to assess if this AI tool could decrease unnecessary ERM referrals to retina specialists. Results A total of 135 optometrists participated. They reported seeing an average of eight ERM cases monthly, referring every fourth case to a specialist. The primary referral reason (84.3%) was to evaluate for surgery. In terms of referral confidence, 34.3% felt fully confident (5/5), and 47.8% slightly less so (4/5). They anticipated that 20% of patients would have a change in management post-consultation with a specialist. When introduced to the concept of an online consultation tool for patient screening, optometrists believed it could reduce their ERM referrals by 40%. Conclusions Optometrists often refer ERM patients to retina specialists. An AI tool for screening ERM referrals, based on presenting vision and OCT images, could significantly lower the number of unnecessary referrals, offering clinical guidance to optometrists. [ Ophthalmic Surg Lasers Imaging Retina 2025;56:166–169.]
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".