Advancing the visibility of outer retinal integrity in neovascular age-related macular degeneration with high-resolution OCT
Bibliographic record
Abstract
OBJECTIVE: To compare the visibility and accessibility of the outer retina in neovascular age-related macular degeneration (nAMD) between 2 OCT devices. METHODS: In this prospective, cross-sectional exploratory study, differences in thickness and loss of individual outer retinal layers in eyes with nAMD and in age-matched healthy eyes between a next-level High-Res OCT device and the conventional SPECTRALIS OCT (both Heidelberg Engineering GmbH, Heidelberg, Germany) were analyzed. Eyes with nAMD and at least 250 nL of retinal fluid, quantified by an approved deep-learning algorithm (Fluid Monitor, RetInSight, Vienna, Austria), fulfilled the inclusion criteria. The outer retinal layers were segmented using automated layer segmentation and were corrected manually. Layer loss and thickness were compared between both devices using a linear mixed-effects model and a paired t test. RESULTS: Nineteen eyes of 17 patients with active nAMD and 17 healthy eyes were included. For nAMD eyes, the thickness of the retinal pigment epithelium (RPE) differed significantly between the devices (25.42 μm [95% CI, 14.24-36.61] and 27.31 μm [95% CI, 16.12-38.50] for high-resolution OCT and conventional OCT, respectively; p = 0.033). Furthermore, a significant difference was found in the mean relative external limiting membrane loss (p = 0.021). However, the thickness of photoreceptors, RPE integrity loss, and photoreceptor integrity loss did not differ significantly between devices in the central 3 mm. In healthy eyes, a significant difference in both RPE and photoreceptor thickness between devices was shown (p < 0.001). CONCLUSION: Central RPE thickness was significantly thinner on high-resolution OCT compared with conventional OCT images explained by superior optical separation of the RPE and Bruch's membrane.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".