The Incidence and Severity of Pediatric Injuries Sustained by Electric Bikes and Powered Scooters
Bibliographic record
Abstract
OBJECTIVES: This study aimed to describe the incidence and severity of electrical bicycle (E-bike)- and power scooter (P-scooter)-related injuries and their secular trends among pediatric patients presenting to a pediatric emergency department (ED). METHODS: This retrospective cohort study of patients aged <18 years who sustained E-bike and P-scooter injuries was performed between 2018 and 2023. We explored trends of severe trauma cases, ED visits, hospitalizations, and surgical interventions. Severity of trauma was rated by either an injury severity score (ISS) of >15 or the patient's need for acute care as defined by intensive care unit (ICU) admission, direct disposition to the operating room, acute interventions performed in the trauma room, and in-hospital death. RESULTS: Of the 1466 pediatric patients who presented to our pediatric ED following P-scooter and E-bike injuries, 216 (14.7%) were hospitalized, with a median age of 14.0 years (interquartile range, 10.5-16.0 years) and male predominance (69.0%). The number of ED visits increased 3.5-fold by study closure, with a parallel increase in hospitalizations, surgical interventions, and severe trauma cases. The relative percentages of severe trauma cases were not significantly different over time. Among hospitalized patients, 3 patients (1.4%) died and 9 (4.1%) required rehabilitation care. CONCLUSIONS: The incidence and severity of E-bike and P-scooter injuries and fatalities continue to increase within the pediatric population. Current personal and road safety regulations are providing inadequate in preventing these injuries, highlighting an urgent need for revision and stricter enforcement.
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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 | 0.001 |
| 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".