Retinal Segmentation as Noninvasive Technique to Demonstrate Hyperplasia in Ataxia of Charlevoix-Saguenay
Bibliographic record
Abstract
PURPOSE: To present a new retinal layer segmentation technique for evaluation of nerve fiber hyperplasia in patients with autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS). METHODS: Five patients with a molecular diagnosis of ARSACS and five age- and sex-matched healthy controls underwent a full ophthalmologic examination, which included a new technique to segment the retinal layers using Spectralis optical coherence tomography (OCT). Images and data were correlated with diffusion tensor color-encoded magnetic resonance imaging maps, diffusion tensor tractographies, and retinal anatomopathologic analysis. RESULTS: Optical coherence tomography evaluation revealed increased thickness in the internal layers of the retina (inner limiting membrane, nerve fiber layer, and ganglion cell layer) in each patient with ARSACS compared with controls. These findings suggest that the presence of neurofilamentous hyperplasia in the retinas of patients with ARSACS correlates with the anatomopathologic findings. CONCLUSIONS: We found evidence of ganglion cell and nerve fiber hyperplasia in the retinas of ARSACS patients. The etiopathogenic mechanisms of this disease thus require reconsideration.
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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 | 0.002 | 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".