The Toronto Tele-Retinal Screening Program for the Elderly in Long-Term Care: A Pilot Project
Bibliographic record
Abstract
Objective: To report the results and feasibility of a pilot expansion of the Toronto Tele-Retinal Screening Program in an elderly long-term care home. Methods: Long term care patients with Type II diabetes mellitus (DM) were screened between April 1, 2022, and July 1, 2022. Demographic and health data were collected through surveys. Results: A total of 28 patients were screened, with 85.7% successfully undergoing retinal imaging. Among imaged patients, 8.3% (2/24) required urgent follow-up. Pathologies identified included uncontrolled glaucoma (4.1%, 1/24), non-proliferative diabetic retinopathy (8.3%, 2/24), and age-related macular degeneration (45.8%, 11/24). The handheld camera successfully screened 60% (3/5) of patients with mobility issues. Overall, 90% (17/19) of patients rated their experience as either "brilliant" or "really good". Discussion: This pilot project demonstrated the necessity for routine eye care in the elderly and the potential for widespread implementation of teleophthalmology in long-term care facilities. With only 14.3% (4/28) of patients unable to be imaged, this program offers a feasible, patient-friendly alternative to in-clinic screening. Future policies and practices in teleophthalmology should consider the unique needs of long-term care residents and the potential for reducing healthcare disparities through such a program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".