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Record W2093559059 · doi:10.1076/chin.9.2.142.14501

Risk for Injury in Preschoolers: Relationship to Attention Deficit Hyperactivity Disorder

2003· article· en· W2093559059 on OpenAlexafffund
Joseph M. Byrne, Harry N. Bawden, Tricia L. Beattie, Nadine A. DeWolfe

Bibliographic record

VenueChild Neuropsychology · 2003
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsIzaak Walton Killam Health Centre
FundersIWK Health Centre
KeywordsImpulsivityPsychologyAttention deficit hyperactivity disorderAttention deficitClinical psychologyInjury preventionPsychiatryDevelopmental psychologyPoison controlMedicineMedical emergency

Abstract

fetched live from OpenAlex

Parental ratings of preschoolers' risk for injury, direct assessment of preschoolers' behavior thought related to risk for injury (e.g., Inattention, impulsivity) and number of documented injuries were examined in preschoolers with Attention Deficit Hyperactivity Disorder (ADHD) and their non-ADHD peers (Control). Of preschoolers with ADHD, 58.3% exhibited behavior which placed them at-risk for physical injury (0% Control), and their performance was significantly poorer on clinic-based tests. Nonetheless, preschoolers with ADHD did not actually sustain significantly more injuries which warranted medical treatment in an emergency department. Although preschoolers with ADHD may be at increased risk for minor injuries, further research is needed to determine whether they more frequently sustain more serious injuries.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.337
Teacher spread0.305 · 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

Citations68
Published2003
Admission routes2
Has abstractyes

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