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Record W2034206446 · doi:10.1177/0883073812448680

Diagnostic Approach to Childhood-Onset Cerebellar Atrophy

2012· article· en· W2034206446 on OpenAlexafffund
Almundher Al‐Maawali, Susan Blasér, Grace Yoon

Bibliographic record

VenueJournal of Child Neurology · 2012
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthRare Disease Foundation
KeywordsAtrophyNeuroimagingCerebellar ataxiaCerebellumMagnetic resonance imagingMedicinePathologyOlivopontocerebellar atrophyAtaxiaAtaxia-telangiectasiaNeuronal ceroid lipofuscinosisNeurosciencePediatricsPsychologyDegenerative diseaseRadiologyDiseaseBiologyGenetics

Abstract

fetched live from OpenAlex

Hereditary ataxias associated with cerebellar atrophy are a heterogeneous group of disorders. Selection of appropriate clinical and genetic tests for patients with cerebellar atrophy poses a diagnostic challenge. Neuroimaging is a crucial initial investigation in the diagnostic evaluation of ataxia in childhood, and the presence of cerebellar atrophy helps guide further investigations. We performed a detailed review of 300 patients with confirmed cerebellar atrophy on magnetic resonance imaging over a 10-year period. A diagnosis was established in 47% of patients: Mitochondrial disorders were most common, followed by neuronal ceroid lipofuscinosis, ataxia telangiectasia, and late-onset GM2 gangliosidosis. We review the common causes of cerebellar atrophy in childhood and propose a diagnostic approach based on correlating specific neuroimaging patterns with clinical and genetic diagnoses.

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)0.004
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 score0.229
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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

Citations44
Published2012
Admission routes2
Has abstractyes

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