Current and Promising Therapies in Autosomal Recessive Ataxias
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Ataxia is clinically characterized by unsteady gait and imbalance. Cerebellar disorders may arise from many causes such as metabolic diseases, stroke or genetic mutations. The genetic causes are classified by mode of inheritance and include autosomal dominant, X-linked and autosomal recessive ataxias. Many years have passed since the description of the Friedreich's ataxia, the most common autosomal recessive ataxia, and mutations in many other genes have now been described. The genetic mutations mostly result in the accumulation of toxic metabolites which causes Purkinje neuron lost and eventual cerebellar dysfunction. Unfortunately, the recessive ataxias remain a poorly known group of diseases and most of them are yet untreatable. CONCLUSION: The aim of this review is to provide a comprehensive clinical profile and to review the currently available therapies. We overview the physiopathology, neurological features and diagnostic approach of the common recessive ataxias. The emphasis is also made on potential drugs currently or soon-to-be in clinical trials. For instance, promising gene therapies raise the possibility of treating differently Friedreich's ataxia, Ataxia-telangiectasia, Wilson's disease and Niemann-Pick disease in the next few years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".