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Attention-Deficit Hyperactivity Disorder (ADHD) and narrative discourse in older adults

2018· article· en· W2906254931 on OpenAlexaff
Rafael Martins Coelho, Paulo Mattos, Rosemary Tannock

Bibliographic record

VenueDementia & Neuropsychologia · 2018
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersInstituto D'Or de Pesquisa e Ensino
KeywordsAttention deficit hyperactivity disorderPsychologyImpulsivityNarrativeSpecific language impairmentNarrative reviewDevelopmental psychologyClinical psychologyPsychiatryLinguistics

Abstract

fetched live from OpenAlex

Attention-Deficit Hyperactivity Disorder (ADHD) encompasses other symptoms besides inattention, hyperactivity, and impulsivity, such as language problems. ADHD can have a non-remitting course and is also found in older individuals, although there are no studies on language problems in elderly individuals with the disorder. OBJECTIVE: To investigate the presence of language impairment in older adults with ADHD. METHODS: Language impairment was investigated in three older ADHD adults, and compared with two matched control subjects using a narrative discourse task. The transcript discourses were evaluated based on the Trabasso Model for discourse analysis, and then processed by the Speech Graph Analysis software. RESULTS: Compared to control subjects, ADHD patient discourse had more Plot components and their networks exhibited more Edges. The patients had higher scores on the Narrative Inefficiency, Density and Diameter Indexes as well as on the Average Clustering Coefficient. The networks of control subjects were sequential, with little or no recursiveness, whereas those of ADHD subjects were convoluted. CONCLUSION: Our results suggest that language deficits described in children, adolescents and young adults with ADHD may persist in older adults with the disorder.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.337
Teacher spread0.315 · 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 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

Citations6
Published2018
Admission routes1
Has abstractyes

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