The Optic Disc Drusen Studies Consortium Recommendations for Diagnosis of Optic Disc Drusen Using Optical Coherence Tomography
Bibliographic record
Abstract
BACKGROUND: Making an accurate diagnosis of optic disc drusen (ODD) is important as part of the work-up for possible life-threatening optic disc edema. It also is important to follow the slowly progressive visual field defects many patients with ODD experience. The introduction of enhanced depth imaging optical coherence tomography (EDI-OCT) has improved the visualization of more deeply buried ODD. There is, however, no consensus regarding the diagnosis of ODD using OCT. The purpose of this study was to develop a consensus recommendation for diagnosing ODD using OCT. METHODS: The members of the Optic Disc Drusen Studies (ODDS) Consortium are either fellowship trained neuro-ophthalmologists with an interest in ODD, or researchers with an interest in ODD. Four standardization steps were performed by the consortium members with a focus on both image acquisition and diagnosis of ODD. RESULTS: Based on prior knowledge and experiences from the standardization steps, the ODDS Consortium reached a consensus regarding OCT acquisition and diagnosis of ODD. The recommendations from the ODDS Consortium include scanning protocol, data selection, data analysis, and nomenclature. CONCLUSIONS: The ODDS Consortium recommendations are important in the process of establishing a reliable and consistent diagnosis of ODD using OCT for both clinicians and researchers.
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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 | 0.199 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".