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Record W2944936314 · doi:10.46278/j.ncacn.20180622

Applicability of Neuropsychological and Psychometric Tests in Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS)

2018· article· en· W2944936314 on OpenAlexaffvenueabout
Kevin Brassard, Geneviève Forgues, Allexe Boivin-Mercier, Cynthia Gagnon

Bibliographic record

VenueNeuropsychologie clinique et appliquée · 2018
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité de SherbrookeUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsRaven's Progressive MatricesDysarthriaSpinocerebellar ataxiaNeuropsychologyPopulationRating scaleAudiologyPsychologyClinical psychologyAtaxiaPhysical medicine and rehabilitationSpasticMedicineCognitionPsychiatryDevelopmental psychologyCerebral palsy

Abstract

fetched live from OpenAlex

Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS) is a degenerative spinocerebellar disease with pyramidal, cerebellar, and neuropathic impairments. Recent studies highlight possible deficits in cognitive functions like language. Psychometric tests selection implies careful consideration due to upper limbs incoordination and dysarthria. The objective of this study is to document the applicability of 37 neuropsychological and 2 psychological tests in 8 individuals with ARSACS aged between 20 and 60 years. All tests were rated on 4 applicability criteria using a 3-level rating scale: A for excellent; B for acceptable; C for reconsider. Most tests posed few or no applicability limits with ARSACS patients. However, certain tests (e.g., Leiter-3 and Raven’s Standard Progressive Matrices) are not recommended due to significant issues related to applicability. These results may help clinicians and researchers working with this population to select evaluations and tests applicable in this population.

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)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
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 head0.073
GPT teacher head0.378
Teacher spread0.304 · 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 designBench or experimental
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

Citations0
Published2018
Admission routes3
Has abstractyes

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