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Record W2083292541 · doi:10.1097/pec.0b013e31827b4631

Body Mass Index and the Odds of Acute Injury in Children

2012· article· en· W2083292541 on OpenAlexaff
J.H. Campbell, Abdullah Alqhatani, Lindsay McRae, Niranjan Kissoon, Quynh Doan

Bibliographic record

VenuePediatric Emergency Care · 2012
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineBody mass indexOverweightOdds ratioLogistic regressionOddsObesityDemographyPediatricsInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The objectives of this study were to determine (1) the association between body mass index (BMI) and acute injury and (2) the association between BMI and bone fracture in children. METHODS: Children 5 to 17 years old were recruited in the emergency department at the British Columbia Children's Hospital. Cases included children treated for an injury, and control subjects were children without an injury in the past 12 months. Participants were administered a questionnaire to derive average activity level and demographic data. Weight and height measurements were taken to calculate BMI. Bivariate and multivariate logistic regressions were used to estimate the odds of injury occurrence by BMI category and the impact of covariates. RESULTS: Logistical regression, after adjusting for age, sex, activity level, and income level, did not reveal an increased association between BMI and acute injury in overweight odds ratio (OR) = 0.90 (0.48-1.70) and obese OR = 1.18 (0.60-2.33) children. Secondary outcome analyses failed to show an increased association between BMI and fracture in overweight OR = 0.44 (0.12, 1.66) and obese OR = 1.02 (0.31, 3.32) children. CONCLUSIONS: This study did not find increasing BMI to be associated with increased acute injury or bone fracture in children.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.006
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.007
GPT teacher head0.297
Teacher spread0.290 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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