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Record W3037693427 · doi:10.4103/jpn.jpn_145_18

Clinical features and molecular genetics of autosomal recessive ataxia in the Turkish population

2020· article· en· W3037693427 on OpenAlexaboutno aff
Faruk İncecik, OzlemM Hergüner, NeslihanO Mungan

Bibliographic record

VenueJournal of Pediatric Neurosciences · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurological diseases and metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtaxiaPediatricsHereditary spastic paraplegiaLeukodystrophyConsanguineous MarriagePopulationConsanguinityJoubert syndromeGeneticsPathologyPsychiatryDiseaseBiology

Abstract

fetched live from OpenAlex

<b>Background:</b> 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. <b>Materials and Methods:</b> Thirty-one children with ARCA were retrospectively analyzed. <b>Results:</b> 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 <sc>l</sc>-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 <i>c</i> 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. <b>Conclusion:</b> 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.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.049
GPT teacher head0.330
Teacher spread0.281 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2020
Admission routes1
Has abstractyes

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