Indigenous Eye Health in the Americas: The Burden of Vision Impairment and Ocular Diseases
Bibliographic record
Abstract
Review of the burden of vision impairment and blindness and ocular disease occurrence in Indigenous Peoples of the Americas. We systematically reviewed findings of the frequency of vision impairment and blindness and/or frequency of ocular findings in Indigenous groups. The database search yielded 2829 citations, of which 2747 were excluded. We screened the full texts of 82 records for relevance and excluded 16. The remaining 66 articles were examined thoroughly, and 25 presented sufficient data to be included. Another 7 articles derived from references were included, summing a total of 32 studies selected. When considering adults over 40 years old, the highest frequencies of vision impairment and blindness in Indigenous Peoples varied from 11.1% in high-income North America to 28.5% in tropical Latin America, whose rates are considerably higher than those in the general population. Most of the ocular diseases reported were preventable and/or treatable, so blindness prevention programs should focus on accessibility to eye examinations, cataract surgeries, control of infectious diseases, and spectacles distribution. Finally, we recommend actions in six areas of attention towards improving the eye health in Indigenous Peoples: access and integration of eye services with primary care; telemedicine; customized propaedeutics; education on eye health; and quality of data.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".