Rates of serious injury from falls in children and adolescents: 10-year retrospective review of data from Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Serious fall-related injuries among children and youth are a preventable public health issue; however, there is a paucity of data and indicators specific to these injuries. We aimed to report an indicator of serious fall injuries within the paediatric population aged 0-19 years in Ontario, Canada. SECONDARY OBJECTIVES: Examine the data by age group, sex and mechanism of injury. METHODS: All fall-related hospitalisations in Ontario among children and youth aged 0-19 years from 2010 to 2019 were analysed. International Classification of Diseases, 10th Revision diagnostic codes defined serious and non-serious paediatric fall injuries. Rates per 100 000 population were calculated. We report serious fall injuries by age group, sex and mechanism of injury. RESULTS: Falls accounted for 22 948 non-serious fall hospitalisations (rate=761.1/100 000; 95% CI 751.3 to 770.9) and 3652 serious fall-related hospitalisations (rate=120.9/100 000; 95% CI 117.0 to 124.8) in Ontario from 2010 to 2019. The highest rate of serious falls among females was observed in the 0-4 age group (150.3/100 000; 95% CI 137.2 to 163.3), while for males, the highest rate was in the 15-19 age group (206.9/100 000; 95% CI 193.0 to 220.7). Overall, males (rate=152.7, 95% CI 146.6 to 158.9) reported higher rates of serious fall injuries compared with females (rate=87.5, 95% CI 82.7 to 92.3). CONCLUSIONS: A significant number of preventable serious fall hospitalisations occurred in Ontario from 2010 to 2019 with the highest rate in older males, followed by younger females. The serious fall indicator identifies important information such as target populations and mechanisms that can be used to prioritise prevention efforts to reduce the burden of fall injury in the paediatric population.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".