Evaluation of retinal morphology of canine sudden acquired retinal degeneration syndrome using optical coherence tomography and fluorescein angiography
Bibliographic record
Abstract
PURPOSE: To describe the optical coherence tomography (OCT) and fluorescein angiography changes in dogs with sudden acquired retinal degeneration syndrome (SARDS). METHODS: Retinal OCT was performed on 10 SARDS dogs and eight control dogs. Tomograms were collected in four quadrants around the optic nerve. Measurements were collected from the photoreceptor layer, the outer nuclear layer, the outer retina, the inner retina and the whole retina thickness in all quadrants. Sodium fluorescein was injected intravenously and serial fundic photographs were collected for a 5 minute period post-injection. RESULTS: In all quadrants, the outer nuclear layer (dorsal temporal P = 0.0000, dorsal nasal P = 0.0001, ventral temporal P = 0.0002, ventral nasal P = 0.000) and outer retina (dorsal temporal P = 0.0001, dorsal nasal P = 0.0002, ventral temporal P = 0.0054, ventral nasal P = 0.0084) measurements were significantly decreased in SARDS dogs. The whole retina thickness was significantly decreased in the dorsal temporal (P = 0.0082) and ventral temporal (P = 0.0428) retina. There were no significant differences in the photoreceptor layer thickness or inner retinal thickness between SARDS and control dogs. All SARDS dogs had a loss of definition of all of the photoreceptor bands on OCT. Two SARDS dogs had multifocal small retinal detachments and one of these dogs exhibited fluorescein leaking at the detachment sites. CONCLUSIONS: The significant reduction in the outer nuclear layer and the loss of band signals in the photoreceptor layers in dogs with SARDS identified on OCT support the previous histopathology findings. Small detachments may occasionally be detected on OCT and they may leak fluorescein.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".