Inherited retinal disease in global Indigenous populations: A scoping review
Bibliographic record
Abstract
Accurate diagnosis is essential for accessing emerging gene-targeted treatments for inherited retinal diseases (IRDs), but many minoritised communities face additional barriers to diagnosis. This scoping review synthesised clinical studies on the prevalence and diagnosis of IRDs among Indigenous Peoples worldwide. Medline, Embase, Global Health, Informit and CINAHL were searched on December 4, 2023. We included articles reporting Indigenous Peoples with IRDs from all global regions published between 1974 and 2023; 73 studies (581 cases) of IRDs in Indigenous Peoples from 24 countries were included, mostly reporting participants indigenous to the Middle East (34 %), Oceania (27 %) and North America (23 %). Studies of specific IRD cases showed geographical or cultural group associations, such as rod-cone dystrophy among the Diné (Navajo Nation) or Bardet-Biedl syndrome in Bedouin populations of the Middle East. With dedicated programs, population-specific IRD gene variants in the Middle Eastern Bedouin populations, New Zealand Māori and other Pacific peoples are the most well-characterised, and this has enabled improved diagnostic approaches. There is limited knowledge of the relative prevalence and support needs for IRDs among most other global Indigenous groups. Engagement, co-designed approaches and collective efforts, including raising awareness, may address challenges limiting equitable access to IRD diagnosis for Indigenous Peoples, facilitating access to emerging treatments.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".