23 Examining the rising trend of paediatric e-scooter injuries: A retrospective study
Bibliographic record
Abstract
Abstract Background The rise of electric scooters (e-scooters) has altered urban transportation by providing an alternative to traditional vehicles. The increased use of these devices globally has raised concerns about their safety, particularly for paediatric populations. Young users are especially at risk of injuries from e-scooters due to factors such as smaller body size, less developed motor skills, and a reduced ability to safely handle these devices. Objectives This study investigates the frequency and nature of e-scooter-related injuries among paediatric patients presenting to the emergency department (ED) of a tertiary paediatric hospital. Design/Methods Retrospective descriptive patient data was collected using the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) database, an injury and poisoning surveillance system. ED visits resulting from e-scooter usage between 2020 and 2024 were identified using the search terms 'e-scooter,' 'electric scooter,' 'electric,' and 'scooter.' Key variables of interest included patient demographics (age and sex), types of injuries sustained, helmet use, time of injury, location, morbidity, and mortality. Results From 2020 to 2024, the frequency of reported yearly ED visits associated with e-scooter usage has risen, with 1 case in 2020, 13 cases in 2021, 27 cases in 2022, 25 cases in 2023, and 46 cases in 2024—an 84.0% increase from the previous year. The mean age of patients was 12.0 ± 3.4 years, and 75.0% of patients were male (n=84). Notably, among patients with reported helmet usage, 78.5% were not wearing helmets (n=62). Furthermore, 25.9% of the patients were admitted to the hospital due to the severity of their injuries (n=29). Head and facial injuries accounted for 36.6% of all injuries (n=75). Among these, facial lacerations were the most common, representing 21.3% (n=16), followed by concussions, which comprised 18.7% (n=14). Fractures constituted 33.7% of all injuries (n=69), with forearm and wrist fractures making up 36.2% of all fractures (n=25). There were no fatalities. Conclusion Our findings align with global trends, highlighting the growing burden of preventable paediatric e-scooter injuries. Given that our province is in the midst of evaluating an e-scooter pilot program, this study provides valuable insights to inform future e-scooter regulations. By examining the injury patterns caused by these devices, this study aims to contribute to improved safety measures for young e-scooter users.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".