The First Case of Autosomal Recessive Cerebellar Ataxia with Prominent Paroxysmal Non‐kinesigenic Dyskinesia Caused by a Truncating <scp>FGF14</scp> Variant in a Turkish Patient
Bibliographic record
Abstract
BACKGROUND: ATX-FGF/SCA27A has been exclusively associated with heterozygous variants in the FGF14 gene, presenting with postural tremor, slowly progressive cerebellar ataxia, and psychiatric and behavioral disturbances. OBJECTIVES: This study describes the first case of ATX-FGF/SCA27A linked to a biallelic frameshift variant in the FGF14 gene. METHODS: Whole-exome sequencing (WES) was conducted using the Illumina NovaSeq 6000 platform, and the identified variant was confirmed using Sanger sequencing. RESULTS: We report the first case of autosomal recessive FGF14-related cerebellar ataxia caused by a c.75del variant resulting in p.Leu26Serfs*51 truncation of the FGF14 protein. This variant was found in a patient born to consanguineous parents and presented with a complex congenital nonprogressive cerebellar disorder accompanied by neurodevelopmental delay, intellectual disability, and prominent drug-responsive paroxysmal non-kinesigenic dyskinesia. Segregation analysis confirmed that the homozygous variant was inherited from heterozygous parents who developed mild gait ataxia and tremor in their 40s. CONCLUSIONS: Biallelic loss-of-function variants in FGF14 are a rare cause of inherited cerebellar ataxia and expand the current genetic spectrum of ATX-FGF14. © 2024 International Parkinson and Movement Disorder Society.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".