Climate Change and the Rising Burden of Ocular Disease: Diagnostic Challenges and Emerging Solutions
Bibliographic record
Abstract
Climate change is an emerging public health crisis that extends beyond systemic diseases to greatly impact ocular health. Increasing ultraviolet (UV) radiation exposure, worsening air pollution, and the accumulation of environmental toxins are contributing to a rise in cataracts, dry eye syndrome (DES), and pterygium. Additionally, climate change is accelerating the spread of infectious eye diseases such as trachoma, onchocerciasis, and fungal keratitis, particularly in vulnerable populations with limited access to healthcare. Diagnosing climate-driven ocular diseases presents unique challenges, as early-stage manifestations are often subtle and overlap with other systemic or age-related conditions. Recent advancements in ophthalmic diagnostics, including AI-assisted imaging, anterior segment optical coherence tomography (AS-OCT), tear fluid biomarkers, and public health campaigns, offer promising solutions for early detection and monitoring of ocular diseases. AI-driven deep learning models have demonstrated potential in detecting cataract changes and environmentally related corneal pathologies with high accuracy. Furthermore, portable imaging technologies have been shown to facilitate screening in underserved regions most affected by climate change. This review explores the intricate relationship between climate change and ocular diseases, focusing on the diagnostic challenges posed by environmental factors and innovations aimed at enhancing patient outcomes. Addressing these issues through improved diagnostic strategies, global health policies, and interdisciplinary collaboration is essential to mitigate the long-term ocular health impacts of climate change.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".