ETDRS-style distance visual acuity charts for Indigenous Canadians: development and validation using Canadian Aboriginal syllabics
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a logMAR Early Treatment Diabetic Retinopathy Study (ETDRS)-style visual acuity chart imprinted with Canadian Aboriginal Syllabics (CAS) optotypes. DESIGN: Prospective, nonrandomized, within-subject analysis. PARTICIPANTS: 22 Indigenous patients from the Ullivik residence (Montreal, QC) for Inuit patients who were able to interpret Latin and CAS characteristics. METHODS: Python and LaTeX scripts were created to generate PDFs of standard ETDRS charts, which could be readily modified for alphabet (CAS or Latin), font, and optotype sizing. We used CAS characters that were preserved across Cree, Ojibwe, and Inuktitut languages. A 60" television screen (4K resolution) was used to display the eye charts to scale. For each subject, the best-corrected visual acuity (BCVA) of each eye was assessed on each chart (4 measurements per patient). RESULTS: The median difference between visual acuities (logMAR) acquired by the Latin and CAS charts was 0 (Q1: -0.08; Q3: 0). Band-Altman analysis revealed a bias of -0.01 (SD: 0.03), which was near zero (indicating favourable agreement), and one outlier (of 22 patients), whose deviation was attributed to random error. The visual acuity differences between eye charts were not statistically significant (p = 0.1). CONCLUSIONS: We developed and validated the first CAS-imprinted ETDRS chart for measuring visual acuity. Given its use of preserved characters across Cree, Ojibwe, and Inuktitut languages, this chart has additional applicability to several Indigenous populations in Canada. Future work should develop near reading cards and other methods of optimizing culturally competent eye care for Indigenous patients.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".