Clinical features and molecular genetics of autosomal recessive ataxia in the Turkish population
Bibliographic record
Abstract
Background: Autosomal recessive cerebellar ataxias (ARCAs) are a heterogeneous group of inherited neurodegenerative disorders. The aim of this study was to present the clinical and genetic features of patients with ataxia complaints and those genetically diagnosed with ARCAs. Materials and Methods: Thirty-one children with ARCA were retrospectively analyzed. Results: Fourteen (45.2%) were boys and 17 (54.8%) were girls with the mean age at onset of symptoms of 46.13 ± 26.30 months (12–120 months). Of the 31 patients, 21 (67.7%) were from consanguineous marriages. Eight patients had Friedreich’s ataxia, five had ataxia telangiectasia, three had l-2-hydroxyglutaric aciduria, three had Joubert syndrome, two had neuronal ceroid lipofuscinosis, two had megalencephalic leukoencephalopathy with subcortical cysts, two had ataxia with ocular motor oculomotor apraxia type 1, one had cytochrome c oxidase deficiency, one had autosomal recessive spastic ataxia of Charlevoix-Saguenay, one had Niemann-Pick type C, one had congenital disorders of glycosylation, one had adrenoleukodystrophy, and one had cobalamin transport disorder. Conclusion: The prevalence of hereditary ataxia can vary among countries. The consanguineous marriage is an important finding in these diseases. These genetic tests will increase the number of ARCA patients diagnosed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".