Distinguishing Central Serous Chorioretinopathy From Neovascular Age-Related Macular Degeneration: A Prospective Study
Bibliographic record
Abstract
Purpose: This article identifies clinical features that differentiate central serous chorioretinopathy (CSR) from neovascular age-related macular degeneration (nAMD) and uses this information to develop a diagnostic tool. Methods: A prospective observational study was conducted of patients with a new diagnosis of CSR, nAMD, or indeterminate presentation. All patients underwent clinical assessment, axial length measurement, enhanced-depth imaging-optical coherence tomography, and intravenous fluorescein angiography. A final consensus diagnosis was derived following review of these factors. Results: < .001). The following odds ratio of CSR reached statistical significance: age 70 and younger (72.00, 95% CI: 11.99-432.50), subfoveal choroidal thickness greater than or equal to 300 µm (33.92, 95% CI: 4.06-283.18), dome-shaped neurosensory detachment (13.24, 95% CI: 3.22-54.45), retinal pigment epithelial changes (0.31, 95% CI: 0.10-0.97), subretinal hyperreflective material (0.11, 95% CI: 0.03-0.42), and fibrovascular pigment epithelial detachment (0.05, 95% CI: 0.01-0.47). A stepwise CSR vs nAMD clinical decision-making algorithm is proposed. Conclusions: Choroidal thickness is increased in CSR when compared with nAMD. The presented odds ratios and the CSR vs nAMD clinical decision-making tool can be applied to distinguish CSR from nAMD.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".