Body mass index and the risk of acute injury in adolescents
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relationship between body mass index (BMI) and acute injury in adolescents. METHODS: An analysis of cross-sectional data from the Canadian Community Health Survey (CCHS) Cycle 3.1 collected by Statistics Canada in 2005 was conducted. The CCHS is a population-based survey that collects information pertaining to the Canadian population health status, health care use and health determinants. The CCHS Cycle 3.1 included 132,221 respondents, of whom 12,317 were 12 to 17 years of age. Multivariate logistic regression was used to estimate the odds of injury occurrence by BMI categories (obese, overweight and neither). RESULTS: The association between overweight and obese BMI levels and injury occurrence in the bivariate model was not significant after adjusting for sex, health status, activity levels and socioeconomic status (OR=1.10 [95% CI 0.97 to 1.24] for overweight and OR=1.12 [95% CI 0.92 to 1.37] for obesity). A subanalysis of those with an injury in the past 12 months found an elevated odds of experiencing multiple injuries in the overweight group, after adjusting for age, health status and physical activity level (OR=1.43 [95% CI 1.16 to 1.77]). CONCLUSION: An increased risk of acute injury in obese and overweight adolescents was not observed. However, the subgroup analysis suggested that multiple injuries are relatively frequent in the overweight BMI group.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".