Clinical and genetic variability among Bulgarian patients with autosomal recessive spastic ataxia of Charlevoix–Saguenay
Bibliographic record
Abstract
BACKGROUND: Autosomal recessive spastic ataxia ofCharlevoix-Saguenay (ARSACS) is a rare neurodegenerative disorder characterizedby early-onset cerebellar ataxia, peripheral sensorimotor neuropathy, and lowerlimb spasticity. We present clinical andgenetic data of the first Bulgarian patients diagnosed with ARSACS by wholeexome sequencing (WES). METHODS: Variant filtering was performed usinglocally established pipeline and the selected variants were analysed by Sangersequencing. All patients underwent clinical examination and testingincluding the standard rating scales for spastic paraplegia and ataxia. RESULTS: Five different SACS gene variants, three of which novel, have been identified inpatients from three different ethnic groups. In addition to the classicalclinical triad, brain MRI revealed cerebellar atrophy, linear pontineT2-hypointensities, and hyperintense rim lateral tothalamus combined with retinal nerve fiber layer thickening on opticcoherence tomography (OCT). CONCLUSION: We expand the mutation, geographic, and phenotypic spectrum of ARSACS, adding Bulgaria to the world map of the disease, and drawing attention to the fact that it is still misdiagnosed. We demonstrated that brain MRI and OCT are necessary clinical tests for ARSACS diagnosis, even if one of the cardinal clinical features is lacking.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".