Rare Spinocerebellar Ataxia Types in Canada: A Case Series and Review of the Literature
Bibliographic record
Abstract
BACKGROUND: There is limited information on rare spinocerebellar ataxia (SCA) variants, particularly in the Canadian population. This study aimed to describe the demographic and clinical features of uncommon SCA subtypes in Canada and compare them with international data. METHODS: We conducted a case series and literature review of adult patients with rare SCA subtypes, including SCA5, SCA7, SCA12, SCA14, SCA15, SCA28, SCA34, SCA35 and SCA36. Data were collected from medical centers in Ontario, Alberta and Quebec between January 2000 and February 2021. RESULTS: We analyzed 25 patients with rare SCA subtypes, with onset ages ranging from birth to 67 years. Infantile and juvenile-onset cases were observed in SCA5, SCA7, SCA14 and SCA34. Most patients presented with gait ataxia, with no significant differences across groups. Additional common features included saccadic abnormalities (22 of 25), dysarthria (19 of 25) and nystagmus (12 of 22, except in SCA7). Less common findings included dystonia (8 of 25), cognitive impairment (7 of 25), tremor (9 of 25) and parkinsonism (3 of 25). CONCLUSION: Our study highlights the heterogeneity of rare SCA subtypes in Canada. Ongoing longitudinal analysis will improve the understanding, management and screening of these disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".