The Toronto Tele-Retinal Screening Program for the Elderly in Long-Term Care: A Pilot Project (Preprint)
Bibliographic record
Abstract
BACKGROUND Mobility challenges, transportation, and finances may serve as barriers to diabetic retinopathy (DR) screening in the growing elderly population in Ontario. Screening of patients in their own nursing homes using teleophthalmology may improve accessibility to DR screening. OBJECTIVE To report the feasibility, results, and patient satisfaction of a pilot expansion of the Toronto Tele-Retinal Screening Program in an elderly long-term care home. METHODS A pilot project was initiated with a cohort of elderly patients with Type II diabetes mellitus (DM) in long-term care who were referred to the Toronto Tele-Retinal Screening Program for DR screening between April 1, 2022 and July 1, 2022. RESULTS A total of 28 patients were screened for DR. Half (14/28) of patients suffered from a fall within the last year and 35% (10/28) were fully dependent for mobility. Overall, 14.3% (4/28) of patients could not be successfully imaged. Of the patients imaged, 8.3% (2/24) were advised to arrange an urgent follow-up. Pathologies identified included uncontrolled glaucoma (4.1%, 1/24), non-proliferative DR (NPDR) (8.3%, 2/24), wet age-related macular degeneration (AMD) (8.3%, 2/24), and dry AMD (37.5%, 9/24). A total of 20.8% (5/24) patients could not undergo tabletop imaging due to mobility issues. Screening of 60% (3/5) of these patients were successful with the handheld camera. Overall, 90% (17/19) of patients rated their subjective screening experience as either “brilliant” or “really good.” CONCLUSIONS This pilot project demonstrated that the implementation of the Toronto Tele-Retinal Screening Program into elderly long-term care homes may mitigate common barriers to eye screening with a good subjective patient experience.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".