ENHANCED DEPTH IMAGING FEATURES OF A CHOROIDAL MACROVESSEL
Bibliographic record
Abstract
In Brief Purpose: To report a case of a choroidal macrovessel imaged using enhanced depth imaging spectral domain optical coherence tomography (EDI-OCT) and describe the choroidal features. Methods: Case report: a 42-year-old man presented with metamorphopsia. Multimodal imaging, including color fundus photography, near-infrared reflectance, and EDI-OCT was used to describe a choroidal macrovessel. Results: Initial ophthalmic examination revealed a serpentine-shaped subretinal pattern deep to the retina near the fovea. When the color image was subjected to a red filter, a large diameter vessel could be seen coursing from the fovea to the temporal macula. EDI-OCT of the choroidal macrovessel revealed a thickened choroid and mild deformation of both the ellipsoid zone and the choroidal–scleral junction. En face spectral domain optical coherence tomography at the level of the choroid demonstrated the anomalous vessel. Conclusion: EDI-OCT and en face optical coherence tomography, used in conjunction with other imaging modalities, can be used to demonstrate the presence and pattern of a choroidal macrovessel. A thickened choroid and overlying outer retinal indentation was observed in association of the choroidal macrovessel. These imaging tools can help distinguish this condition from other diagnoses with a similar appearance, such as ophthalmomyiasis. The structure of a choroidal macrovessel is analyzed using enhanced depth imaging spectral domain optical coherence tomography (EDI-OCT). Enlarged vessel caliber, indentation of the choroidal–scleral junction, and increased choroidal thickness are key features reported for the first time. EDI-OCT may allow for a noninvasive assessment and diagnosis of this condition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".