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Record W2129027871 · doi:10.1080/15402002.2014.940106

Association Between Childhood Sleep-Disordered Breathing and Disruptive Behavior Disorders in Childhood and Adolescence

2014· article· en· W2129027871 on OpenAlexafffund
Evelyn Constantin, Nancy Low, Erika N. Dugas, Igor Karp, Jennifer O’Loughlin

Bibliographic record

VenueBehavioral Sleep Medicine · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsInstitut National de Santé Publique du QuébecUniversité de MontréalCentre Hospitalier de l’Université de MontréalMcGill University
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchCanada Research ChairsMcGill University
KeywordsSleep disordered breathingObstructive sleep apneaOddsOdds ratioCohortMedicinePediatricsSleep apneaGeneration RPopulationPsychiatryAssociation (psychology)Clinical psychologyCohort studyPsychologyLogistic regressionInternal medicine

Abstract

fetched live from OpenAlex

We examined the association between sleep-disordered breathing (SDB) and disruptive behavior disorders in 605 children participating in a population-based cohort study. Nineteen percent of children snored (sometimes or often) and 10% had obstructive sleep apnea (OSA) symptoms. Thirteen percent had an ADHD diagnosis or symptoms and 5-9% had behavioral problems or a conduct disorder. Snoring or OSA symptoms were associated with a twofold difference in the odds of ADHD diagnosis or symptoms. OSA symptoms were associated with a threefold to fourfold difference in the odds of behavioral problems or conduct disorder. Clinicians should consider inquiring about SDB in children with disruptive behavior disorders and should also consider disruptive behavior disorders as potential sequelae of SDB.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.287
Teacher spread0.268 · 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 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

Citations45
Published2014
Admission routes2
Has abstractyes

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