<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 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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".