Optometric Referrals to Retina Specialists: Evaluation and Triage via Teleophthalmology
Bibliographic record
Abstract
A retrospective noncomparative consecutive case series was conducted to evaluate the clinical outcomes of a novel teleophthalmology program linking optometrists to retina specialists in Alberta, Canada. One hundred seventy-one patients, referred by optometrists via teleophthalmology to a group retina practice between June 2004 and May 2006 underwent stereoscopic, mydriatic digital photography. Images were transmitted to a secure Web server and analyzed by a retina specialist. Diagnosis and recommendations were sent back to the optometrist and, if necessary, patients were referred for additional testing and clinical evaluation. A chart review of all clinical encounters was performed and the data was tabulated. Demographic features, diagnosis, testing, treatment, distance and time traveled by patient, durations between telemedicine referral, teleophthalmology consultation, in-person consultation, testing, and treatment were recorded. One hundred seventy patients were assessed via teleophthalmology for a total of 190 consultations. Eighty-nine patients (52.0%) required conventional in-person consultation with a referral completion success of 92.1% (82 patients). Fifty of these patients underwent additional diagnostic testing including fluorescein angiography (41), optical coherence tomography (14), laboratory testing (5), visual fields (2), carotid Doppler ultrasound (2), and ocular ultrasound (2). Twenty-five patients required surgical or medical treatment including focal argon laser (10), photodynamic therapy (8), panretinal photocoagulation (2), vitrectomy (2), scleral buckle (1), and other procedures (8). Average wait time between telemedicine referral and teleophthalmology review of images by the retina specialist was 1.9 days (maximum = 20 days). For those patients requiring office evaluation, the average wait time between teleophthalmology referral and in-person evaluation was 25.1 days. Twenty-one of the 25 patients (84.0%) requiring treatment underwent examination, testing, and treatment in a single day. When compared to conventional consultation methods, teleophthalmology reduced average travel distance and time by 219.1 km and 2.7 hours, respectively. Teleophthalmology reduced office visits to the retina specialist by 48% while improving the efficiency of clinical examination, testing, and treatment. Patients benefited through reduced travel time and distance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".