International Consensuses and Guidelines on Diagnosing and Managing Coats Disease by the Academia Retina Internationalis (ARI), the Asia-Pacific Vitreo-retina Society (APVRS), and the Academy of the Asia-Pacific Professors of Ophthalmology (AAPPO)
Bibliographic record
Abstract
PURPOSE: Coats disease is a rare, retinal vascular disorder characterized by telangiectasias, aneurysmal dilations, and progressive exudative retinal detachment. Limited understanding of the disease warranted the need to identify controversial issues through an extensive literature search and a debate and discussion among international panels of experts. METHODS: Extensive literature search was done on multiple aspects of the disease-classification patterns, disease, Coats-plus, and Coats-like response. Other key factors included the etiology, possible genetic patterns, relationship with other vascular disorders, the role of inflammatory factors and vascular endothelial growth factor (VEGF) in the pathogenesis, diagnostic features, and complications. Considering the varying treatment patterns followed, imaging modalities, clinical findings, differential diagnosis, treatment options, prognostic factors, and emerging concepts in management were all covered in the search. Eighteen experts were included to opine on questions spanning classification, pathogenesis, diagnostic and treatment methods, prognostic controversies, and newer concepts in disease management. RESULTS: Of the 52 questions in 7 sections, the experts arrived at a consensus for 48 (92.3%) statements (with 75% voted as strong agreement or agreement). Most experts agreed on the suggested classification, diagnosis, and prognosis. The controversy, however, remained in questions regarding association with other vascular disorders, treatment of stages 3 and 4, possible benefits of newer anti-VEGF agents, and the role of artificial intelligence. CONCLUSIONS: These debates reflect the rarity of the condition, complex pathophysiology, and the challenges of treating a progressive blinding disease primarily affecting children. Hence, further studies are warranted, especially in areas that have not reached a consensus.
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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 | 0.071 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".