Pathophysiology of outer retinal corrugations: Imaging dataset and mechanical models
Bibliographic record
Abstract
This article presents high-resolution swept-source optical coherence tomography (SS-OCT) imaging data used to elaborate a mechanical model that elucidates the formation of outer retinal corrugations (ORCs) in rhegmatogenous retinal detachments (RRD). The imaging data shared in the repository and presented in this article is related to the research paper entitled “Outer Retinal Corrugations in Rhegmatogenous Retinal Detachment: The Retinal Pigment Epithelium-Photoreceptor Dysregulation Theory” (Muni et al., AJO, 2022). The dataset consists of 69 baseline cross-sectional SS-OCT scans from 66 patients that were assessed for the presence of ORCs and analyzed considering the clinical features of each case. From the 66 cases, we selected SS-OCT images of 4 RRD patients with visible ORCs and no cystoid macular edema (CME) to validate the mechanical model. We modelled the retina as a composite material consisting of the outer retinal layer (photoreceptor layer) and the inner retinal layer (the part of the retina that excludes the photoreceptor layer) with thicknesses To and Ti and elastic modulus Eo and Ei, respectively. The thickness of the outer and inner retinal layers and the relative increase in the length of the outer retinal layer (γ) were measured from the SS-OCT images. Measurements from the SS-OCT images of patients with RRD demonstrated a 30% increase (γ=0.3) in the length of the outer retinal layer and a 400% increase in the thickness of the outer retinal layer (To). Using the mathematical model, Eo/Ei ranged between 0.05 to 0.5 to result in ORCs with a similar frequency to those observed in the SS-OCT scans.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".