Association of Predominantly Peripheral Lesions on Ultra-Widefield Imaging and the Risk of Diabetic Retinopathy Worsening Over Time
Why this work is in the frame
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Bibliographic record
Abstract
Importance: Ultra-widefield (UWF) imaging improves the ability to identify peripheral diabetic retinopathy (DR) lesions compared with standard imaging. Whether detection of predominantly peripheral lesions (PPLs) better predicts rates of disease worsening over time is unknown. Objective: To determine whether PPLs identified on UWF imaging are associated with increased disease worsening beyond the risk associated with baseline Early Treatment Diabetic Retinopathy Study (ETDRS) Diabetic Retinopathy Severity Scale (DRSS) score. Design, Setting, and Participants: This cohort study was a prospective, multicenter, longitudinal observational study conducted at 37 US and Canadian sites with 388 participants enrolled between February and December 2015. At baseline and annually through 4 years, 200° UWF-color images were obtained and graded for DRSS at a reading center. Baseline UWF-color and UWF-fluorescein angiography (FA) images were evaluated for the presence of PPL. Data were analyzed from May 2020 to June 2022. Interventions: Treatment of DR or diabetic macular edema was at investigator discretion. Main Outcomes and Measures: Predominantly peripheral lesions were defined as DR lesions with a greater extent outside vs inside the 7 standard ETDRS fields. Primary outcome was disease worsening defined as worsening 2 steps or more on the DRSS or receipt of DR treatment. Analyses were adjusted for baseline DRSS score and correlation between 2 study eyes of the same participant. Results: Data for 544 study eyes with nonproliferative DR (NPDR) were analyzed (182 [50%] female participants; median age, 62 years; 68% White). The 4-year disease worsening rates were 45% for eyes with baseline mild NPDR, 40% for moderate NPDR, 26% for moderately severe NPDR, and 43% for severe NPDR. Disease worsening was not associated with color PPL at baseline (present vs absent: 38% vs 43%; HR, 0.78; 95% CI, 0.57-1.08; P = .13) but was associated with FA PPL at baseline (present vs absent: 50% vs 31%; HR, 1.72; 95% CI, 1.25-2.36; P < .001). Conclusions and Relevance: Although no association was identified with color PPL, presence of FA PPL was associated with greater risk of disease worsening over 4 years, independent of baseline DRSS score. These results suggest that use of UWF-FA to evaluate retinas peripheral to standard ETDRS fields may improve the ability to predict disease worsening in NPDR eyes. These findings support use of UWF-FA for future DR staging systems and clinical care to more accurately determine prognosis in NPDR eyes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it