International consensuses and controversies on causes, diagnosis and management of diabetic macular edema (DME)
Bibliographic record
Abstract
Diabetic macular edema (DME) is the most common cause of vision-threatening diabetic retinopathy (VTDR) with an increasing prevalence tied to the global epidemic in diabetes. Despite significant advances, the management of DME remains a dynamic field with many unresolved controversies. Optical coherence tomography (OCT) allows objective assessment, however, correlation between vision and morphological changes can be inconsistent, causing disagreements on treatment strategies. DME is a complex disease with multifactorial pathophysiological pathways, leading to heterogenous treatment responses. There is a lack of standardized definition of treatment "non-response" and protocol for switching to second-line or adjuvant treatments. New anti-vascular endothelial growth factor (anti-VEGF) drugs and multi-targeted therapies seem to demonstrate improved durability, but long-term data is not yet available. Research in artificial intelligence (AI) is developing rapidly, however, rigorous appraisal of its reliability and generalizability are necessary before its implementation. Significant vision loss from DME in pregnant women, young children and elderly patients with systemic comorbidities are challenging conundrums. An international panel of experts (IPE) comprising 36 experts from 16 countries formulated and voted on the consensus statements in 5 key areas: 1) Diagnostic controversies around classification and imaging; 2) Treatment controversies; 3) Management paradigm between protocol-based and individualized approaches; 4) Emerging controversies in novel therapeutics and AI application, and 5) Special considerations for specific patient populations. There is an imminent need for mutual agreement on the best-possible approach to DME management in order to promote the optimal patient outcomes and to identify specific issues that require prioritization of resources and research.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".