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Record W1850853577 · doi:10.1093/pch/12.9.749

The relationship between childhood behaviour disorders and unintentional injury events

2007· article· en· W1850853577 on OpenAlexafffund
Beth S. Bruce, Susan Kirkland, Daniel A. Waschbusch

Bibliographic record

VenuePaediatrics & Child Health · 2007
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsDalhousie University
FundersIWK Health CentreDalhousie University
KeywordsMedicineInjury preventionRetrospective cohort studyPediatricsEmergency departmentPoison controlAttention deficit hyperactivity disorderOccupational safety and healthSuicide preventionCohortCohort studyPsychiatryEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the controversy regarding the existence of a relationship between behavioural disorders and unintentional injuries in children. DESIGN: A retrospective cohort analysis of children between six and 19 years of age, who were diagnosed with attention deficit hyperactivity disorder (ADHD) only (n=955), ADHD plus conduct problems (CP) (n=160), or CP only (n=234), were compared with a nondisorder group of children (n=21,308) for unintentional injury events resulting in a physician office or emergency room visit, or hospitalization. RESULTS: The risk of an injury event was greater among children with a behaviour disorder diagnosis and severity of injury varied among the behaviour disorder groups. Children with ADHD were the only disorder group at increased risk for all three injury outcomes. Children with a comorbid diagnosis were at a greater risk for both minor and more serious emergency injury visits, and children with CP only were at greatest risk for the most serious injuries (hospital admission). CONCLUSIONS: These findings provide further support that children with ADHD are at an increased risk for not only hospitalized injury events but also minor injury events. In addition, these findings provide evidence that serious injuries are more likely to be experienced by children with CP.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.038
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.345
Teacher spread0.321 · 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.

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

Citations43
Published2007
Admission routes2
Has abstractyes

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