Macular fibrosis in neovascular AMD: inter-reader and intermodality variability across four imaging modalities
Bibliographic record
Abstract
OBJECTIVE: To assess inter-reader and intermodality variability in quantifying macular fibrosis in patients with neovascular age-related macular degeneration (AMD) using 4 imaging modalities: color fundus photography (CFP), near-infrared reflectance (NIR), structural optical coherence tomography (OCT), and MultiColor imaging. DESIGN: Prospective, cross-sectional case series. PARTICIPANTS: Thirty eyes of 30 patients with neovascular AMD and macular fibrosis, previously treated with anti-vascular endothelial growth factor therapy. METHODS: Imaging was performed using CFP, NIR, structural OCT, and MultiColor modalities. Two masked graders evaluated the size of macular fibrosis using each modality. The study assessed inter-reader agreement using the intraclass correlation coefficient (ICC), coefficient of variation (CV), and 95% coefficient of repeatability. Intermodality variability was analyzed using repeated measures ANOVA and pairwise comparisons. RESULTS: Structural OCT demonstrated the highest inter-reader agreement (ICC = 0.983; CV = 0.04), while NIR exhibited the lowest (ICC = 0.461; CV = 0.26). The median macular fibrosis size was largest on structural OCT (4.87 mm²) and smallest on MultiColor imaging (2.03 mm²). Significant differences were observed between imaging modalities, with fibrosis measurements from different modalities not consistently comparable. CONCLUSIONS: Structural OCT is the most reliable modality for quantifying macular fibrosis in neovascular AMD. The observed intermodality variability highlights the need for standardized criteria in fibrosis assessment across imaging techniques, as differences can impact clinical interpretation and management.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".