Look in or book in: The case for type 2 diabetes remission to prevent diabetic retinopathy
Bibliographic record
Abstract
Background: Diabetic retinopathy (DR) remains the leading cause of legal blindness in 18- to 74-year-old Americans and in most developed nations. Screening for DR has increased minimally over four decades.Aim: Primary care physicians are critical to improve both visual and systemic outcomes in patients with diabetes. Diabetic retinopathy screening affords clinicians the opportunity to discuss type 2 diabetes (T2D) remission with patients. Primary care is well positioned to manage, and lower risks, of the systemic-associated diseases predicted by DR. The goal of this review was to assess the current literature on DR, new technology to enhance primary care-based screening, and the science and practical application of diabetes remission. A two-pronged strategy, bringing attention to ophthalmologists the potential of diabetes remission, and family physicians, the importance of retinopathy screening, may reduce the prevalence of blindness in patients with diabetes.Methods: Embase, PubMed, Google Scholar, AMED, and MEDLINE databases were searched using keywords ‘diabetic retinopathy; diabetic retinopathy screening, diabetes remission, diabetes reversal, and AI and diabetic retinopathy’.Results: Robust literature now exists on diabetes remission and international consensus panels are aligning on the strategies and the definition.Conclusion: Diabetic retinopathy remains the leading cause of legal blindness. Novel primary care friendly imaging would benefit nearly half of Americans from earlier detection and treatment of DR still not receiving such care. The most powerful way a primary care clinician could impact DR would be assisting in making the T2D go into remission. Prevention or slowing of progression of DR would greatly improve both visual and systemic outcomes patients with diabetes.Contribution: This article highlights the importance of addressing DR and metabolic health to reduce not only the eye effects of T2D but the multisystem complications.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".