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Record W2005110161 · doi:10.1016/s0924-9338(13)76875-5

1931 – Attention Problems In Childhood And Substance Use 18 Years Later

2013· article· en· W2005110161 on OpenAlexaff
Cédric Galéra, Jean‐Baptiste Pingault, Éric Fombonne, Grégory Michel, Emmanuel Lagarde, Manuel Bouvard, Maria Melchior

Bibliographic record

VenueEuropean Psychiatry · 2013
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsMcGill UniversityUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsPsychologySubstance usePsychiatry

Abstract

fetched live from OpenAlex

Introduction Attention Deficit/Hyperactivity Disorder (ADHD) has been linked to substance use disorders later in life. However, the unique contribution of ADHD is not clarified yet. Insufficient research in this area has considered first substance use, associated behavioral problems and female gender in longitudinal and community settings. Aims To study the association between childhood attention problems and substance use 18 years later. Method Using a French community sample of 1103 youths followed from 1991 to 2009, we tested associations between childhood attention problems (dimensional constructs) and substance use between ages 22 and 35, adjusting for potential childhood and family confounders. Results Individuals with high levels of childhood attention problems presented higher rates of substance use (regular tobacco smoking, alcohol abuse/dependence, cannabis problematic use, cocaine lifetime use). However, when taking into account other childhood behavioral problems, early substance use, academic difficulties and family adversity, childhood attention problems were only related to regular tobacco smoking and cocaine lifetime use. Conclusions This longitudinal community-based study shows that, except for tobacco and cocaine, the association between childhood attention problems and substance use is confounded by a range of early risk factors.

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 categoriesInsufficient 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 score0.003
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.254
Teacher spread0.231 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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