Electric scooter injury and trauma in Edmonton: a multicentre prospective and retrospective observational study
Bibliographic record
Abstract
BACKGROUND: The introduction of rentable electric scooters (e-scooters) has been associated with injury presentations to emergency departments (EDs). Our objective was to determine the incidence and severity of injuries from rentable e-scooters among adults presenting to EDs in a northern urban region. METHODS: Adults presenting to all Edmonton EDs with injuries related to rentable e-scooters during 3 summers (2019-2021) were eligible for inclusion. We identified e-scooter charts using multiple sources: administrative data, trauma registry, and text-based triage searching. Two independent reviewers assessed each patient for study inclusion; disagreements were resolved by content experts. Trained researchers performed data extraction and descriptive statistical analysis. RESULTS: We included 759 e-scooter-related injury presentations. The median age was 28 years, males and females were almost equally represented, 20% presented by ambulance, and 14% were triaged as urgent. Most patients had multiple injuries (62%), with fractures (32%) and head injuries (17%) being common. Helmet use was infrequent (2%) and concurrent substance use was prevalent (26%). Admission to hospital was uncommon (5.5%); however, 30% of patients presenting to an ED with an e-scooter injury required further follow-up, with 9% undergoing surgery within 30 days of their index visit. CONCLUSION: Injuries related to rentable e-scooters are increasingly common. Most injured patients have multiple injuries and require investigations, and a third require further management. These injuries represent substantial burdens to patients and the health care system in Canada. Injury prevention strategies should be considered to reduce injuries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".