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Record W1969820533 · doi:10.1093/arclin/acs058

Magnetic Resonance Imaging Predictors of Executive Functioning in Patients with Pediatric-Onset Multiple Sclerosis

2012· article· en· W1969820533 on OpenAlexafffund
Christine Till, Carole Ho, Ameeta Dudani, Daniel García-Lorenzo, D. Louis Collins, Brenda Banwell

Bibliographic record

VenueArchives of Clinical Neuropsychology · 2012
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill UniversitySickKids FoundationUniversity of TorontoMontreal Neurological Institute and HospitalHospital for Sick ChildrenYork University
FundersCanadian Institutes of Health ResearchMultiple Sclerosis Society of Canada
KeywordsExecutive functionsNeuropsychologyWorking memoryPsychologyExecutive dysfunctionMultiple sclerosisNeuropsychological assessmentClinical psychologyAudiologyCognitionMedicinePsychiatry

Abstract

fetched live from OpenAlex

Executive functions (EFs) are vulnerable to disruption in pediatric-onset multiple sclerosis (MS) patients. We describe the pattern and correlates of executive dysfunction in 34 adolescents with MS on neuropsychological tests and the parent version of the Behavior Rating Inventory of Executive Function (BRIEF). The adolescents with MS performed lower than age-matched controls in several areas of executive functioning, with 44% of patients being impaired on the Trail Making Test-Part B. On the BRIEF, problems in working memory and planning/organization were identified in the patient group compared with controls, particularly in patients with a younger age at disease onset. Task performance and parent-ratings of EF skills were strongly related to whole brain and regional brain volume metrics and, to a lesser extent, T(2)-weighted lesion volume. Working memory and attention switching are at greatest risk of impairment. Results support the inclusion of neuropsychological assessment alongside parent-report measures of EF skills in childhood-onset MS.

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 score0.035
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.057
GPT teacher head0.341
Teacher spread0.284 · 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

Citations29
Published2012
Admission routes2
Has abstractyes

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