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Record W2014847648 · doi:10.1192/bjp.bp.111.092130

Clinical and cognitive characteristics of children with attention-deficit hyperactivity disorder, with and without copy number variants

2011· article· en· W2014847648 on OpenAlexaff
K. Langley, Joanna Martin, Sharifah Shameem Agha, Charlotte Davies, Peter Holmans, Nigel Williams, Michael J. Owen, Michael O‘Donovan, Anita Thapar

Bibliographic record

VenueThe British Journal of Psychiatry · 2011
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsChild, Adolescent and Family Mental Health
FundersWellcome Trust
KeywordsCopy-number variationComorbidityIntellectual disabilityAttention deficit hyperactivity disorderPsychopathologyCognitionAttention deficitPsychiatryNeurodevelopmental disorderPsychologyMedicineClinical psychologyGeneticsAutismGene

Abstract

fetched live from OpenAlex

BACKGROUND: Submicroscopic, rare chromosomal copy number variants (CNVs) contribute to neurodevelopmental disorders but it is not known whether they define atypical clinical cases. AIMS: To identify whether large, rare CNVs in attention-deficit hyperactivity disorder (ADHD) are confined to a distinct clinical subgroup. METHOD: A total of 567 children with ADHD aged 5-17 years were recruited from community clinics. Psychopathology was assessed using the Child and Adolescent Psychiatric Assessment. Large, rare CNVs (>500 kb, <1% frequency) were defined from single nucleotide polymorphism data. RESULTS: Copy number variant carriers (13.6%) showed no differences from non-carriers in ADHD symptom severity, symptom type, comorbidity, developmental features, family history or pre-/perinatal markers. The only significant difference was a higher rate of intellectual disability (24% v. 9%, χ(2) = 15.5, P = 0.001). Most CNV carriers did not have intellectual disability. CONCLUSIONS: Large, rare CNVs are not restricted to an atypical form of ADHD but may be more highly enriched in children with cognitive problems.

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)0.000
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 score0.024
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.303
Teacher spread0.280 · 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

Citations33
Published2011
Admission routes1
Has abstractyes

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