<scp>Myoclonus‐Ataxia</scp> Syndromes: A Diagnostic Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it