Current Choroidal Imaging Findings in Central Serous Chorioretinopathy
Bibliographic record
Abstract
BACKGROUND: Central serous chorioretinopathy (CSCR) is a chorioretinal disease affecting mostly middle age males. It is marked by the serous detachment of the neurosensory layer at the macula. This review of the literature provides a framework of the current characteristic/relevant imaging findings of CSCR. Although the pathogenesis of CSCR is unclear, the choroid plays a major role and its changes are fundamental to the diagnosis and treatment of CSCR. METHODS: A systematic literature search focusing on current multimodal imaging for CSCR was performed. Only articles reporting on original clinical data were selected, studies in a language other than English were included only if an English abstract was provided. Additional sources included articles cited in the references list of the first selected articles. We deduced imaging findings based on current and relevant literature on the topic. RESULTS: We found that sub foveal choroidal thickness (SFCT) and choroidal vascularity index (CVI) were greater in eyes with acute CSCR than in eyes with chronic CSCR or normal eyes. There was increased choroidal thickness (CT) in the macula compared to peripapillary region. In healthy eyes, the highest CVI was found in the nasal region followed by the inferior, temporal, and superior quadrant. The area with the least CVI was the macula. In eyes with CSCR, 100% had asymmetric dominant vortex veins compared to 38% in normal eyes. CONCLUSION: Choroidal imaging has advanced the diagnosis of CSCR. This has led to numerous imaging biomarkers like CVI, CT, and hyper-reflective dots for early detection and possible prognostication of CSCR. More techniques like wide field scans and en face imaging are being employed to characterize the choroid in CSCR.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".