<scp>Myoclonus‐Ataxia</scp> Syndromes: A Diagnostic Approach
Bibliographic record
Abstract
BACKGROUND: A myriad of disorders combine myoclonus and ataxia. Most causes are genetic and an increasing number of genes are being associated with myoclonus-ataxia syndromes (MAS), due to recent advances in genetic techniques. A proper etiologic diagnosis of MAS is clinically relevant, given the consequences for genetic counseling, treatment, and prognosis. OBJECTIVES: To review the causes of MAS and to propose a diagnostic algorithm. METHODS: A comprehensive and structured literature search following PRISMA criteria was conducted to identify those disorders that may combine myoclonus with ataxia. RESULTS: A total of 135 causes of combined myoclonus and ataxia were identified, of which 30 were charted as the main causes of MAS. These include four acquired entities: opsoclonus-myoclonus-ataxia syndrome, celiac disease, multiple system atrophy, and sporadic prion diseases. The distinction between progressive myoclonus epilepsy and progressive myoclonus ataxia poses one of the main diagnostic dilemmas. CONCLUSIONS: Diagnostic algorithms for pediatric and adult patients, based on clinical manifestations including epilepsy, are proposed to guide the differential diagnosis and corresponding work-up of the most important and frequent causes of MAS. A list of genes associated with MAS to guide genetic testing strategies is provided. Priority should be given to diagnose or exclude acquired or treatable disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".