Ethnic disparities in inherited retinal degenerations
Bibliographic record
Abstract
OBJECTIVE: Inherited retinal degenerations (IRD) are clinically heterogeneous. There has been little study of the influence of ethnicity on IRD phenotypes. We aim to assess clinical and genetic variability between differing ethnic groups affected by IRD. DESIGN: Retrospective cohort study. PARTICIPANTS: Patients with genetically resolved IRD (ABCA4, USH2A, RPGR) at a single centre (University of British Columbia). METHODS: Clinical and genetic data were contrasted between ethnic groups (Caucasian, East Asian, South Asian, Indigenous, African) and between Caucasians and non-Caucasians. RESULTS: 143 patients met the inclusion criteria. Caucasians were over-represented (76%). For ABCA4, East Asians most commonly had bullseye maculopathy, while classic Stargardt disease predominated in other ethnicities; cataract was less prevalent in non-Caucasians (p = 0.001). For USH2A, most non-Caucasians had non-syndromic IRD, while Caucasians were 50% isolated and 50% Usher syndrome. Hyperautofluorescent rings were more common in non-Caucasians (p = 0.027). In RPGR, best-corrected visual acuity was worse for Caucasians (logMAR 0.76 ± 0.69) than non-Caucasians (0.49 ± 0.30; p = 0.047), and myopia was greatest in South Asians (-9.56 ± 0.27 D vs -3.82 ± 4.05 D; p < 0.001). Twenty-one novel genetic variants were identified, and only 3.3% (5/154) of genetic variants were shared between ethnic groups. CONCLUSIONS: Clinical and genetic differences are apparent between ethnic groups, even within "common" IRD genotypes. Awareness of these different retinal and extra-retinal (e.g., myopia, less favourable VA) features is critical to facilitate diagnostic accuracy and optimal clinical care, including access to novel therapies. Further work to expand the genetic reference databases for non-Caucasian ethnic groups is needed to facilitate equitable access to diagnosis and treatment for IRD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".