Frequency of GAA- <i>FGF14</i> Ataxia in a Large Cohort of Brazilian Patients With Unsolved Adult-Onset Cerebellar Ataxia
Bibliographic record
Abstract
Objectives Intronic FGF14 GAA repeat expansions have recently been found to be a common cause of hereditary ataxia (GAA-FGF14 ataxia; SCA27B). The global epidemiology and regional prevalence of this newly reported disorder remain to be established. In this study, we investigated the frequency of GAA-FGF14 ataxia in a large cohort of Brazilian patients with unsolved adult-onset ataxia. Methods We recruited 93 index patients with genetically unsolved adult-onset ataxia despite extensive genetic investigation and genotyped the FGF14 repeat locus. Patients were recruited across 4 different regions of Brazil. Results Of the 93 index patients, 8 (9%) carried an FGF14 (GAA)≥250 expansion. The expansion was also identified in 1 affected relative. Seven patients were of European descent, 1 was of African descent, and 1was of admixed American ancestry. One patient carrying a (GAA)376 expansion developed ataxia at age 28 years, confirming that GAA-FGF14 ataxia can occur before the age of 30 years. One patient displayed episodic symptoms, while none had downbeat nystagmus. Cerebellar atrophy was observed on brain MRI in 7 of 8 patients (87%). Discussion Our results suggest that GAA-FGF14 ataxia is a common cause of adult-onset ataxia in the Brazilian population, although larger studies are needed to fully define its epidemiology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".