A novel sacsin mutation in a Japanese woman showing clinical uniformity of autosomal recessive spastic ataxia of Charlevoix-Saguenay
Bibliographic record
Abstract
Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) was originally described among French Canadians in the Charlevoix-Saguenay-Lac-Saint-Jean region of Quebec (OMIM 270550).1 The gene responsible for ARSACS was identified as sacsin, and frameshift (8585 deletion T, 2805X) and nonsense (C7245T, R2355X) mutations were reported in Quebec.2 Recently, patients with other mutations have been described in countries elsewhere.3 These showed not only the spastic ataxia with peripheral neuropathy recognised in Quebec, but some additional features as well.3 Here we describe a female Japanese ARSACS patient with a novel nonsense mutation (C3774T, Q1198X), which resulted in a shorter truncated protein than those of the French Canadian patients. We report the details of her clinical and genetic data, and discuss the correlation between mutations and phenotypes in ARSACS. The patient was a 39 year old woman who first walked at 12 months. Her gait was normal in early childhood. A spastic gait started at nine years of age, but she made no complaint about it for many years. After the age of 35 she complained of unsteadiness in her gait and clumsiness in her hands. Her gait disturbance progressed and she visited our clinic at the age of 37. Consanguinity was not identified in her parents. Family members (parents and one brother) and other relatives have …
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| 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".