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Record W2739775117 · doi:10.3389/fnagi.2017.00260

Adult ADHD: Risk Factor for Dementia or Phenotypic Mimic?

2017· article· en· W2739775117 on OpenAlexafffund
Brandy L. Callahan, Daniel Bierstone, Donald T. Stuss, Sandra E. Black

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2017
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of TorontoHotchkiss Brain InstituteHealth Sciences CentreUniversity of CalgaryOntario Brain InstituteHeart and Stroke FoundationSunnybrook Health Science Centre
FundersUniversity of TorontoSunnybrook Research InstituteAlzheimer's Association
KeywordsDementiaAnxietyCognitionPsychiatryPsychologyClinical psychologyDiseaseDepression (economics)Attention deficit hyperactivity disorderCognitive declineMedicine

Abstract

fetched live from OpenAlex

Attention-deficit hyperactivity disorder (ADHD) has historically been considered a disorder of childhood and adolescence. However, it is now recognized that ADHD symptoms persist into adulthood in up to 60% of individuals. Some of the cognitive symptoms that characterize ADHD (inability to provide sustained attention or mental effort, difficulty organizing or multi-tasking, forgetfulness) may closely resemble symptoms of prodromal dementia, also often referred to as mild cognitive impairment (MCI), particularly in patients over age 50. In addition to the overlap in cognitive symptoms, adults with ADHD and those with MCI may also share a number of behavioral and psychiatric symptoms, including sleep disturbances, depression, and anxiety. As a result, both syndromes may be difficult to distinguish clinically in older patients, particularly those who present to memory clinics with subjective cognitive complaints and fear the onset of a neurodegenerative process: is it ADHD, MCI, or both? Currently, it is unclear whether ADHD is associated with incipient dementia or is being misdiagnosed as MCI due to symptom overlap, as there exist data supporting either possibility. Here, we aim to elucidate this issue by outlining three hypothetical ways in which ADHD and MCI might relate to each other, providing an overview of the evidence relevant to each hypothesis, and delineating areas for future research. This is a question of considerable importance, with implications for improved diagnostic specificity of early dementia, improved accuracy of disease prevalence estimates, and better identification of individuals for targeted treatment.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.058
GPT teacher head0.340
Teacher spread0.282 · 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 designTheoretical or conceptual
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

Citations67
Published2017
Admission routes2
Has abstractyes

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