The European Eye Epidemiology spectral‐domain optical coherence tomography classification of macular diseases for epidemiological studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: The aim of the European Eye Epidemiology (E3) consortium was to develop a spectral-domain optical coherence tomography (SD-OCT)-based classification for macular diseases to standardize epidemiological studies. METHODS: A European panel of vitreoretinal disease experts and epidemiologists belonging to the E3 consortium was assembled to define a classification for SD-OCT imaging of the macula. A series of meeting was organized, to develop, test and finalize the classification. First, grading methods used by the different research groups were presented and discussed, and a first version of classification was proposed. This first version was then tested on a set of 50 SD-OCT images in the Bordeaux and Rotterdam centres. Agreements were analysed and discussed with the panel of experts and a final version of the classification was produced. RESULTS: Definitions and classifications are proposed for the structure assessment of the vitreomacular interface (visibility of vitreous interface, vitreomacular adhesion, vitreomacular traction, epiretinal membrane, full-thickness macular hole, lamellar macular hole, macular pseudo-hole) and of the retina (retinoschisis, drusen, pigment epithelium detachment, hyper-reflective clumps, retinal pigment epithelium atrophy, intraretinal cystoid spaces, intraretinal tubular changes, subretinal fluid, subretinal material). Classifications according to size and location are defined. Illustrations of each item are provided, as well as the grading form. CONCLUSION: The E3 SD-OCT classification has been developed to harmonize epidemiological studies. This homogenization will allow comparing and sharing data collection between European and international studies.
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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 | 0.003 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it