MétaCan
Menu
Back to cohort

Validating neuropsychological subtypes of ADHD: how do children <i>with</i> and <i>without</i> an executive function deficit differ?

2010· article· en· W1638877925 on OpenAlexaff
Rikke Lambek, Rosemary Tannock, Søren Dalsgaard, Anegen Trillingsgaard, Dorte Damm, Per Hove Thomsen

Bibliographic record

VenueJournal of Child Psychology and Psychiatry · 2010
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPsychologyNeuropsychologyExecutive functionsAttention deficit hyperactivity disorderAttention deficitExecutive dysfunctionClinical psychologyNeuropsychological assessmentDevelopmental psychologyCognitionPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: The study investigates behavioural, academic, cognitive, and motivational aspects of functioning in school-age children with attention-deficit/hyperactivity disorder (ADHD) with and without an executive function deficit (EFD). METHOD: Children with ADHD--EFD (n = 22) and children with ADHD + EFD (n = 26) were compared on aspects of ADHD behaviour, school functioning, general cognitive ability, intra-individual response variability, affective decision-making, and delay aversion. RESULTS: Children with ADHD--EFD and children with ADHD + EFD were comparable in terms of ADHD symptomatology and school functioning. However, children with ADHD + EFD had significantly lower IQ and more intra-individual response variability than no EFD counterparts. Children with ADHD alone appeared more delay averse on the C-DT task than children with ADHD + EFD. CONCLUSIONS: Some children with ADHD were primarily characterised by problems with executive functions and variability others by problems with delay aversion supporting multiple pathway models of ADHD. Given the exploratory nature of the study, results are in need of replication.

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)0.000
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 score0.010
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.315
Teacher spread0.294 · 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

Citations88
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Child Psychology and PsychiatrySame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207