Multimodal Imaging of Unilateral Benign Yellow Dot Maculopathy
Bibliographic record
Abstract
Purpose: To describe the multimodal imaging findings associated with benign yellow dot maculopathy. Methods: A case report was analyzed. Results: A healthy 42-year-old White man was evaluated after several weeks of photopsias and an inferior retinal tear in the right eye. The tear was treated with laser retinopexy. A fundus examination showed numerous small, yellow, subretinal lesions in and around the macula of the right eye only. The patient had no known systemic conditions and an unremarkable family and ocular history, with 20/20 visual acuity in both eyes. Multimodal imaging findings were consistent with the newly described phenotype of benign yellow dot maculopathy. Conclusions: This is the second known case of unilateral benign yellow dot maculopathy. Distinct multimodal imaging findings between unilateral cases and bilateral cases may suggest differences in their etiology and manifestation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".