Body Mass Index and the Odds of Acute Injury in Children
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".