DISCREPANCY BETWEEN FLUORESCEIN ANGIOGRAPHY AND OPTICAL COHERENCE TOMOGRAPHY IN DETECTION OF MACULAR DISEASE
Bibliographic record
Abstract
In Brief Purpose: To compare high-resolution optical coherence tomography (OCT) and fluorescein angiography (FA) in detection of macular edema (ME) of various etiologies. Methods: In a retrospective study over a 12-month period at one retina center, data for consecutive eyes that had undergone simultaneous conventional FA (HRA; Heidelberg Engineering, Vista, CA) and StratusOCT (Carl Zeiss Meditec, Dublin, CA) to rule out ME were reviewed. A subset of patients underwent additional examination with extremely high-resolution (6-μm)/ultrahigh-speed spectral OCT/scanning laser ophthalmoscopy (OTI, Inc., Toronto, Ontario, Canada). Results: Of 1,272 eyes, 1,208 (94.97%) had the finding of ME or subretinal fluid confirmed by both techniques. There were 49 eyes (3.86%) for which FA showed dye leakage in the macular area and OCT showed normal foveal contour. Of 10 eyes in this group that underwent imaging with ultrahigh-speed spectral OCT/scanning laser ophthalmoscopy, 8 had subtle diffuse lucencies in the retina. For 15 eyes (1.17%), OCT showed intraretinal and subretinal fluid, which was missed by FA. Conclusions: Both FA and high-resolution OCT are highly sensitive techniques and correlate well in detection of ME. However, there is a small chance that when performed alone they might miss existing subtle ME. Both fluorescein angiography and high-resolution optical coherence tomography are highly sensitive techniques and correlate well in detection of macular edema. However, a small chance exists that when performed alone they might miss existing subtle macular edema.
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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 | 0.004 | 0.020 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".