B.06 Whole exome sequencing in genetic ataxias associated with cerebellar atrophy: the Canadian experience
Bibliographic record
Abstract
Background: Cerebellar atrophy is characterized by loss of cerebellar tissue, with evidence on brain imaging of enlarged interfolial spaces compared to the foliae. Genetic ataxias associated with cerebellar atrophy are a heterogeneous group of disorders. We investigated the prevalence in Canada and the diagnostic yield of whole exome sequencing (WES) for this group of conditions. Methods: Between 2011 and 2017, WES was performed in 91 participants with cerebellar atrophy as part of one of two national research programs, Finding of Rare Genetic Disease Genes (FORGE) or Enhanced Care for Rare Genetic Diseases in Canada (Care4Rare). Results: A genetic diagnosis was established in 58% of cases (53/91). Pathogenic variants were found in 24 known genes, providing a diagnosis for 46/53 participants (87%), and in four novel genes, accounting for 7/53 cases (13%). 38/91 cases (42%) remained unsolved. The most common diagnoses were channelopathies in 12/53 patients (23%) and mitochondrial disorders in 9/53 (17%). Inheritance was autosomal recessive in the majority of cases. Additional clinical findings provided useful clues to some of the diagnoses. Conclusions: This is the first report on the prevalence of genetic ataxias associated with cerebellar atrophy in Canada, and the utility of WES for this group of conditions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".