Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 2000 to 2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by diabetic retinopathy and their proportion of the total number of vision-impaired individuals. METHODS: Data from population-based studies on eye diseases between 1980 to 2018 were compiled. Meta-regression models were performed to estimate the prevalence of blindness (presenting visual acuity <3/60) and moderate or severe vision impairment (MSVI; <6/18 to ≥3/60) attributed to DR. The estimates, with 95% uncertainty intervals [UIs], were stratified by age, sex, year, and region. RESULTS: In 2020, 1.07 million (95% UI: 0.76, 1.51) people were blind due to DR, with nearly 3.28 million (95% UI: 2.41, 4.34) experiencing MSVI. The GBD super-regions with the highest percentage of all DR-related blindness and MSVI were Latin America and the Caribbean (6.95% [95% UI: 5.08, 9.51]) and North Africa and the Middle East (2.12% [95% UI: 1.55, 2.79]), respectively. Between 2000 and 2020, changes in DR-related blindness and MSVI were greater among females than males, predominantly in the super-regions of South Asia (blindness) and Southeast Asia, East Asia, and Oceania (MSVI). CONCLUSIONS: Given the rapid global rise in diabetes and increased life expectancy, DR is anticipated to persist as a significant public health challenge. The findings emphasise the need for gender-specific interventions and region-specific DR healthcare policies to mitigate disparities and prevent avoidable blindness. This study contributes to the expanding body of literature on the burden of DR, highlighting the need for increased global attention and investment in this research area.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it