Urgent Virtual Eye Assessments During the COVID-19 Pandemic
Bibliographic record
Abstract
Purpose: We aimed to evaluate the effectiveness and safety of a virtual eye assessment triage system implemented in response to COVID-19. Patients and Methods: We conducted a retrospective cross-sectional study using a consecutive sample of all virtual assessments conducted from March 24 to June 7, 2020 at a single ophthalmology center in Toronto, ON, Canada. Visual acuity and smartphone photographs were uploaded to an electronic assessment website. All patients were virtually triaged to an email or phone consult. Patient outcomes and satisfaction were assessed with a quality assurance survey. Primary outcome measures were the incidence of unplanned additional in-person visits and changes in treatment. Results: We performed 1535 virtual assessments. Of the triage pathways, 15% received an email consult only and 85% received a phone consult. Subsequently, 15% required an in-person assessment, 3% were referred elsewhere, and 0.1% were sent to the emergency. Presentations were most commonly cornea (52%) and retina (25%). They were non-urgent in 68% of cases and no pharmacologic treatment was required for 49%. Of 397 patients that responded out of 653 patients surveyed, 4% had an unplanned additional visit to the emergency, after which two patients underwent urgent retinal surgery and one patient underwent urgent glaucoma surgery. Two patients (0.5%) had a minor change in treatment. Conclusion: As routine regular in-person visits were not possible during the COVID-19 lockdown, virtual eye assessments provided an opportunity to triage patients. Virtual assessments have the potential to reduce in-person visits, but caution must be exercised to not miss vision-threatening conditions.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".