Retinal Architecture in Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS): Insights into Disease Pathogenesis and Biomarkers
Bibliographic record
Abstract
Background \nAutosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) causes unique retinal abnormalities, which have not been systematically investigated. \n \nObjective \nTo deeply phenotype the retina in ARSACS in order to better understand its pathogenesis and identify potential biomarkers. \n \nMethods \nWe evaluated 29 patients with ARSACS, 66 with spinocerebellar ataxia (SCA), 38 with autosomal recessive cerebellar ataxia (ATX), 22 with hereditary spastic paraplegia (SPG), 21 cases of papilledema, and 20 healthy controls (total n = 196 subjects). Participants underwent visual acuity assessment, intraocular pressure measurement, fundoscopy, and macular and peripapillary optical coherence tomography (OCT). Macular layers thicknesses in ARSACS were compared with those of age-matched healthy controls. Ophthalmologists analyzed the scans for abnormal signs in the different patient groups. Linear regression analysis was conducted to look for associations between retinal changes and age, age at onset, disease duration, and Scale for the Assessment and Rating of Ataxia (SARA) scores in ARSACS. \n \nResults \nOnly patients with ARSACS exhibited peripapillary retinal striations (82%) on fundoscopy, and their OCT scans revealed foveal hypoplasia (100%), sawtooth appearance (89%), papillomacular fold (86%), and macular microcysts (18%). Average peripapillary retinal nerve fiber layer (pRNFL) was thicker in ARSACS than in SCA, ATX, SPG, and controls; a cut-off of 121 μm was 100% accurate in diagnosing ARSACS. All macular layers were thicker in ARSACS when compared to healthy controls. RNFL thickness in the inferior sector of the macula positively correlated with SARA scores. \n \nConclusions \nRetinal abnormalities are highly specific for ARSACS, and suggest retinal hyperplasia due to abnormal retinal development. OCT may provide potential biomarkers for future clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".