Ataxias cerebelosas autosómicas recesivas. Clasificación, aspectos genéticos y fisiopatología
Bibliographic record
Abstract
INTRODUCTION AND DEVELOPMENT: Autosomal recessive cerebellar ataxias (ARCA) are a heterogeneous group of rare neurological disorders involving both central and peripheral nervous system, and in some case other systems and organs. They use to have early onset before the age of 20. Based on pathogenic mechanisms five main types may be distinguished: congenital (developmental disorder), mitochondrial ataxias, ataxias associated with metabolic disorders, ataxias with a DNA repair defect, and degenerative ataxia with unknown pathogenesis. The most frequent in Caucasian population are Friedreich ataxia and ataxia-telangiectasia. Other forms are much less common, and include abetaliproteinemia, ataxia with vitamin E deficiency (AVED), ataxia with oculomotor apraxia types 1 (AOA1) and 2 (AOA2), early onset cerebellar ataxia with retained reflexes, Charlevoix-Saguenay spastic ataxia, and Joubert syndrome. The prevalence of ARCA has been estimated to 7 in 100,000 inhabitants. These diseases are due to mutations in specific genes, some of which and its encoded proteins have been identified, such as FRDA (frataxin) in Friedreich ataxia, APTX (aprataxin) in AOA1, alphaTTP (alpha-tocopherol transfer protein) in AVED, and STX (senataxin) in AOA2. Due to autosomal recessive inheritance, previous familial history of affected individuals unlikely. CONCLUSIONS: Most of these cerebellar ataxias have no specific treatment with exception of the ataxia associated with deficiency coenzyme Q10 and abetalipoproteinemia. Clinical diagnosis must be confirmed by ancillary tests such as neuroimaging (magnetic resonance, scanning), electrophysiological examination, and mutation analysis when the causative gene has been identified. Correct clinical and genetic diagnosis is important for appropriate prognosis and genetic counseling and, in some instances, pharmacological treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".