Comprehensive Teleoptometry Exams in Canada: A Proposed Clinical Framework
Bibliographic record
Abstract
Purpose: Many high-income countries like Canada are faced with unmet needs regarding the availability of access to eye care, especially in rural, remote, or northern areas. Teleoptometry has the potential to improve access to primary eye care and help prevent, detect, diagnose and treat uncorrected refractive error and sight-threatening eye diseases. Given the rapid adoption of technology for virtual care during the COVID pandemic, the existing teleoptometry guidelines in Canada are limited in scope and may lead to uncertainty for optometrists practicing remote care. The purpose of this paper is to build a scaffold that highlights the similarities and differences between an in-person comprehensive eye examination and one that is delivered through comprehensive teleoptometry in Canada. This proposed clinical framework draws from both the existing published literature and the clinical experience of the authors. This paper discusses issues for teleoptometry including patient consent, efficiency, delegation, training and the patient pathway including referral protocols when indicated. Results: Comprehensive teleoptometry eye exams are very similar to in-person eye exams, but they depend more on the assistance of an in-person technician/optometric assistant. The exams include delegated tests performed by an optometric assistant and tests controlled remotely by the optometrist like refraction. However, other tests that require clinical judgment to execute or interpret should be performed by the optometric assistant under the direct supervision of the remote optometrist using live video. The remote optometristbe able to repeat any test during videoconferencing. Conclusion: Teleoptometry is a tool optometrists can use to reach patients that struggle to access an in-person eye exam. This evidence-informed, draft framework provides a point of reference for discussion by the Federation of Optometric Regulatory Authorities of Canada and the provincial regulatory authorities to protect the public and increase access through the delivery of remote primary eye care in Canada.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".