Beauty of Black and White: Autofluorescence-aided Differentiation of Serpiginous Choroiditis from Tubercular Serpiginous-Like Choroiditis
Bibliographic record
Abstract
INTRODUCTION: FAF is non-invasive, and important adjunctive tool to evaluate the progression of lesions in patients with SC. FAF can even help distinguish probable etiology by specific pattern recognition. The current index study analyzed and reported the strength of specific patterns to be more representative of SC or TB SLC. OBJECTIVE: To characterize fundus autofluorescence (FAF) images for differentiating serpiginous choroiditis (SC) from tubercular serpiginous-like choroiditis (TB SLC). METHODOLOGY: The index study is a retrospective comparative analysis of FAF images of 25 consecutive patients, 11 with TB SLC and 14 with SC. The diagnosis of SC was made based on the clinical appearance and FAF findings, while TB SLC was additionally considered in patients with positive laboratory investigations and/or radiological tests for tuberculosis (TB) exposure or infection and therapeutic response to anti-tubercular therapy. The characteristic features evaluated on FAF images were centrality, multifocality, and parapapillary involvement of the lesion with or without extension. RESULT: Twenty-five patients (13 males, 12 females) with a mean age of 46.2 (SD 10.08) years were enrolled in the study. SC lesions were more central (ρ=0.92) and confluent (ρ=0.774). Parapapillary involvement was found to be associated with SC (ρ=0.690), and with extensions of the lesions along the arcades or the macular region, the association increased (ρ=0.786). Multifocality with peripheral lesions was negatively associated with SC (ρ=- 0.831). CONCLUSION: Centrally involving lesions with confluency on FAF is strongly associated with SC. Parapapillary involvement alone is considered characteristic for SC, but the current study has demonstrated that extension of this lesion along the arcades or the macular region is even more characteristic for SC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".