Clustering of burns with other types of injury in patients younger than 10 years of age
Bibliographic record
Abstract
BACKGROUND: Childhood burns may cluster with other trauma, but the relationship between burns and other injuries is poorly understood. The objective of this study was to determine the association between burns and other types of injury hospitalization in children younger than age 10 years. METHODS: We carried out a multicenter cohort study of 4,262 patients aged younger than 10 years with burns who were matched with 40,518 controls in Quebec, Canada. The main exposure measure was a burn requiring hospital treatment. The main outcome was hospitalization for other types of injury anytime between birth and age 10 years. We used Cox regression models adjusted for patient characteristics to assess the association (hazard ratio; 95% confidence interval) between burns and risk of other injury hospitalization. RESULTS: Patients with burns had a greater rate of hospitalization for other types of injury than controls (7.6 vs 5.1 per 1,000 person-years), equivalent to 1.46 times greater risk (95% confidence interval, 1.29-1.65). Compared with controls, patients with burns were 1.61 times more likely to have an injury hospitalization between age 0 and <5 years (95% confidence interval, 1.39-1.88) and 1.25 times more likely between age 5 and <10 years (95% confidence interval, 1.02-1.52). Patients with burns were 4.74 times more likely to have been hospitalized for a maltreatment injury before their burn (95% confidence interval, 2.68-8.38). CONCLUSION: Children with burns are at high risk of hospitalization for other injuries before age 10 years. A better understanding of how pediatric injuries cluster with burns may help prevent child trauma.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".