<scp>Genotype–phenotype</scp> relations for episodic ataxia genes: <scp>MDSGene</scp> systematic review
Bibliographic record
Abstract
BACKGROUND: Most episodic ataxias (EA) are autosomal dominantly inherited and characterized by recurrent attacks of ataxia and other paroxysmal and non-paroxysmal features. EA is often caused by pathogenic variants in the CACNA1A, KCNA1, PDHA1, and SLC1A3 genes, listed as paroxysmal movement disorders (PxMD) by the MDS Task Force on the Nomenclature of Genetic Movement Disorders. Little is known about the genotype-phenotype correlation of the different genetic EA forms. METHODS: We performed a systematic review of the literature to identify individuals affected by an episodic movement disorder harboring pathogenic variants in one of the four genes. We applied the standardized MDSGene literature search and data extraction protocol to summarize the clinical and genetic features. All data are available via the MDSGene protocol and platform on the MDSGene website (https://www.mdsgene.org/). RESULTS: Information on 717 patients (CACNA1A: 491, KCNA1: 125, PDHA1: 90, and SLC1A3: 11) carrying 287 different pathogenic variants from 229 papers was identified and summarized. We show the profound phenotypic variability and overlap leading to the absence of frank genotype-phenotype correlation aside from a few key 'red flags'. CONCLUSION: Given this overlap, a broad approach to genetic testing using a panel or whole exome or genome approach is most practical in most circumstances.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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; both teacher heads agree on what is shown here.
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".