MétaCan
Menu
Back to cohort

Current and Promising Therapies in Autosomal Recessive Ataxias

2018· review· en· W2800740578 on OpenAlexafffund
Vincent Picher‐Martel, Nicolas Dupré

Bibliographic record

VenueCNS & Neurological Disorders - Drug Targets · 2018
Typereview
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsAtaxiaAtaxia-telangiectasiaMedicineDiseaseCerebellar ataxiaBioinformaticsNeuroscienceGeneticsBiologyPathologyPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.328
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCNS & Neurological Disorders - Drug TargetsSame topicGenetic Neurodegenerative DiseasesFrench-language works237,207