Temperament and fracture in preschool-aged children
Bibliographic record
Abstract
OBJECTIVES: Approximately one-half of all children will sustain a fracture before adulthood. Understanding the factors that place a child at increased risk of fracture is necessary to inform effective injury prevention strategies. The purpose of this study was to examine the association between temperament and fracture risk in preschool-aged children. METHODS: Children aged 3 to 6 years who were diagnosed with a fracture were recruited from the Hospital for Sick Children Fracture Clinic. Using a retrospective case-control study design, the 148 cases were frequency-matched by age and sex to 426 controls from the TARGet Kids primary care paediatric cohort. The Childhood Behaviour Questionnaire, a 36-item caregiver response questionnaire was used to assess three of the following temperament factors: surgency (e.g., high activity level), negative affect (e.g., anger, fear, discomfort) and effortful control (e.g., attentional focusing). RESULTS: Unadjusted logistic models demonstrated no association between children with previous fracture and higher scores of surgency (unadjusted odds ratio [OR]=1.06, 95% confidence interval [CI]: 0.84, 1.34), negative affect (unadjusted OR=1.15, 95% CI: 0.93, 1.42) or effortful control (unadjusted OR=0.80, 95% CI: 0.63, 1.03). Further, models adjusted for covariates also demonstrated no significant association with surgency (1.00, 95% CI: 0.78, 1.29), negative affect (1.09, 95% CI: 0.86, 1.37) and effortful control (0.80, 95% CI: 0.61, 1.05). CONCLUSION: None of the three main temperament types identified by the Childhood Behaviour Questionnaire were associated with an increase in fracture risk.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".