Intronic<i>FGF14</i>GAA repeat expansions are a common cause of ataxia syndromes with neuropathy and bilateral vestibulopathy
Bibliographic record
Abstract
Background Intronic GAA repeat expansions in the fibroblast growth factor 14 gene (FGF14) have recently been identified as a common cause of ataxia with potential phenotypic overlap withRFC1-related cerebellar ataxia, neuropathy and vestibular areflexia syndrome (CANVAS). Our objective was to report on the frequency of intronicFGF14GAA repeat expansions in patients with an unexplained CANVAS-like phenotype. Methods We recruited 45 patients negative for biallelicRFC1repeat expansions with a combination of cerebellar ataxia plus peripheral neuropathy and/or bilateral vestibulopathy (BVP), and genotyped theFGF14repeat locus. Phenotypic features of GAA-FGF14-positive versus GAA-FGF14-negative patients were compared. Results Frequency ofFGF14GAA repeat expansions was 38% (17/45) in the entire cohort, 38% (5/13) in the subgroup with cerebellar ataxia plus polyneuropathy, 43% (9/21) in the subgroup with cerebellar ataxia plus BVP and 27% (3/11) in patients with all three features. BVP was observed in 75% (12/16) of GAA-FGF14-positive patients. Polyneuropathy was at most mild and of mixed sensorimotor type in six of eight GAA-FGF14-positive patients. Family history of ataxia (59% vs 15%; p=0.007) was significantly more frequent and permanent cerebellar dysarthria (12% vs 54%; p=0.009) significantly less frequent in GAA-FGF14-positive than in GAA-FGF14-negative patients. Age at onset was inversely correlated to the size of the repeat expansion (Pearson’s r, −0.67; R2=0.45; p=0.0031). Conclusions GAA-FGF14-related disease is a common cause of cerebellar ataxia with polyneuropathy and/or BVP, and should be included in the differential diagnosis ofRFC1CANVAS and disease spectrum.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".