Injury patterns and circumstances associated with electric scooter collisions: a scoping review
Bibliographic record
Abstract
BACKGROUND: Electric scooters are personal mobility devices that have risen in popularity worldwide since 2017. Emerging reports suggest that both riders and other road users, such as pedestrians and cyclists, have been injured in electric scooter-associated incidents. We undertook a scoping review of the current literature to evaluate the injury patterns and circumstances of electric scooter-associated injuries. METHODS: A scoping review of literature published from 2010 to 2020 was undertaken following accepted guidelines. Relevant articles were identified in Medline, Embase, SafetyLit and Transport Research International Documentation using terms related to electric scooters, injuries and incident circumstances. Supplemental searches were conducted to identify relevant grey literature (non-peer-reviewed reports). RESULTS: Twenty-eight peer-reviewed studies and nine grey literature records were included in the review. The current literature surrounding electric scooter-associated injuries mainly comprises retrospective case series reporting clinical variables. Factors relating to injury circumstances are inconsistently reported. Findings suggest that the head, upper extremities and lower extremities are particularly vulnerable in electric scooter falls or collisions, while injuries to the chest and abdomen are less common. Injury severity was inconsistently reported, but most reported injuries were minor. Low rates of helmet use among electric scooter users were noted in several studies. CONCLUSION: Electric scooters leave riders vulnerable to traumatic injuries of varying severity. Future work should prospectively collect standardised data that include information on the context of the injury event and key clinical variables. Research on interventions to prevent electric scooter injuries is also needed to address this growing area of concern.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".