Safety And Risk Factors Associated With Electric Scooter Use Globally: A Literature Review
Bibliographic record
Abstract
Electric kick scooters (e-scooters) are a form of micro-mobility devices that have been implemented in city streets worldwide as a viable travel solution. E-scooter companies have launched in over 100 U.S cities and various international cities, including Paris, Berlin, London, Rome, Madrid, Singapore, Auckland, Tel Aviv, and Brisbane. On January 1st 2020, Ontario launched its pilot program to permit e-scooters onto provincial roads. Due to the implementation and recent growth of this new technology, it is important to evaluate what is already known about e-scooter use and what remains to be discovered. We conducted a literature review to understand the general prevalence of e-scooter usage, common injury patterns, demographics of patients commonly involved in e-scooter injuries, and risk factors associated with injuries. We also sought to understand the current legislation surrounding e-scooter use in Ontario, other provinces across Canada, and other countries. Common injuries included: extremity fractures, facial fractures, lacerations and head injuries (including concussions and intracranial hemorrhages). Most commonly injured riders were men between 20- 40 years old, and our findings indicate that limited helmet use and acute alcohol intoxication may contribute to e-scooter injuries. These findings can help to direct future research questions and prepare primary care and emergency room physicians for the potential surge in e-scooter use here in Ontario, Canada.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".