Editorial: Advances in the research of diabetic retinopathy
Bibliographic record
Abstract
Advances in the research of diabetic retinopathyDiabetes remains a planetary crisis with its prevalence estimated to increase by nearly 50% in the next 25 years (1, 2) One of the main challenges for the diabetic patients is the development of chronic complications, leading to end organ damage.Chronic diabetic complications are a major cause of mortality and morbidity for the people living with diabetes.Retinal damage in diabetics, also known as diabetic retinopathy (DR) is a leading cause of blindness in working-aged adults (3, 4).Although DR begins with asymptomatic hyperglycemic damage to the retinal microvasculature, in particularly endothelial cells, it eventually causes a symphony of abnormalities at various levels, creating cellular dysfunction and damage, ultimately leading to functional and structural changes in the retina that result in vision impairment and blindness (5-7).Current treatment approaches serve as band-aid solutions that address the root of the problem in a very limited perspective (8, 9).To develop a solid preventive and therapeutic approach for DR, a better understanding of this disease is essential.This Research Topic presents a large number of articles including original research as well as review to improve our understanding of DR.The specific topics represent various levels of complexities.The publications address issues ranging from the molecular level to cellular level to animal levels and finally to DR patients.The articles investigate and discuss specific pathogenetic mechanisms, effects of current treatment modalities and treatment outcomes.It was also to be noted that this collection also identifies potential upcoming treatment modalities and diagnostic approach using various RNA molecules as well as application of artificial intelligence and machine learning for DR diagnosis and assessment of prognosis.Among the review topics Zhang et al. compared effectiveness of panretinal photocoagulation alone along with in combination of anti VEGF treatment.Guo et al. discussed uric acid abnormalities, an understudied area in DR.Similarly, Zheng et al. reviewed another relatively unappreciated topic, i.e, relationship of sleep quality with risk Frontiers in Endocrinology frontiersin.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.027 | 0.024 |
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".