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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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes3
Has abstractyes

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