Abnormal retinal thickening is a common feature among patients with ARSACS-related phenotypes: Table 1
Bibliographic record
Abstract
Autosomal-recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) was first described among French Canadian patients from Quebec presenting with a stereotypical triad of early-onset cerebellar ataxia, spastic paraplegia and peripheral neuropathy. Two recurring pathogenic mutations in the SACS gene were subsequently identified in these families in keeping with a mutational founder event in a geographically isolated population.1 However, ARSACS is being increasingly recognised worldwide as an important cause of inherited ataxia.2 Interestingly, non-Quebec patients can show strikingly variable features marked by a lack of spasticity, cognitive impairment and a delayed age of onset.2 Given the heterogeneous clinical picture that can be associated with SACS mutations, the identification of ancillary features linked with these genetic defects could prove particularly useful in prioritising the most appropriate lines of investigations when confronted with a suspected case of ARSACS. Although prominent retinal hypermyelination is thought to be a characteristic manifestation of classical ARSACS among Quebec patients, this ophthalmological finding has only been described infrequently in patients from Europe, Asia and the Middle East.2 A recent case report has even further argued that retinal ‘hypermyelination’ in ARSACS is a pathologically misleading term that should be …
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".