Evidence map›Paper›PMID 36922264›Full record

ArticleChinese journal of traumatology = Zhonghua chuang shang za zhi2023

Electric scooter injuries: Incidence and injury patterns at a level I trauma center.

Nina D Fisher, Ekenedilichukwu Nwakoby, Hunter Hernandez, Toni M McLaurin

Open access · diamondAbstract read
In one paragraph

Article in Chinese journal of traumatology = Zhonghua chuang shang za zhi, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
5.2field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 18 citations in OpenAlex.

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  8. Burden and Patterns of Electric Scooter-Related Injuries: Insights From 2 Polish Emergency Departments.Medical science monitor : international medical journal of experimental and clinical research · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Nina D FisherDepartment of Orthopedic Surgery, Langone Orthopedic Hospital, New York University, New York, United States. Electronic address: Nina.Fisher@nyulangone.org.
Ekenedilichukwu NwakobyDepartment of Orthopedic Surgery, Langone Orthopedic Hospital, New York University, New York, United States.
Hunter HernandezDepartment of Orthopedic Surgery, Langone Orthopedic Hospital, New York University, New York, United States.
Toni M McLaurinDepartment of Orthopedic Surgery, Langone Orthopedic Hospital, New York University, New York, United States; Department of Orthopedic Surgery, Health+Hospitals/Bellevue New York City, New York, United States.
New York University Langone Orthopedic Hospital · USBellevue Hospital Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeElectric scooters (e-scooters) have become an increasingly popular mode of public transportation in recent years. As the incidence of related injuries rises, it is important to understand specific fracture patterns unique to e-scooters and electric bikes (e-bikes) to help guide management. The purpose of this study was to review the prevalence and describe specific fracture patterns of e-scooter and e-bike related injuries at the busiest level 1 trauma center in the borough of Manhattan.

methodsChart review to determine mechanism of injury was performed on all patients for whom an orthopedic consult was requested from 1/1/2021 to 12/31/2021. All patients whose injuries were sustained due to an e-scooter or e-bike were further reviewed for demographics, injury characteristics including fracture pattern, and definitive injury management. Any patients who had an orthopedic consult placed for a reason other than an acute injury were excluded. Descriptive statistics are reported as frequency (percentage) for categorical variables and means for continuous variables.

resultsOf the 1815 orthopedic consults requested, 1357 (74.8%) were for acute injury management. Of those with acute injuries, 119 (8.8%) sustained 136 e-scooter or e-bike related injuries. There were 92 (77.3%) males at an average age of (33.8 ± 15.7) years. Approximately one-fifth of all patients presented in June 2021 (26, 21.8%). There was a 9.2% rate of open fractures. The 136 injuries were evenly split between the upper and lower extremities, with 57 (47.9%) upper extremity, 57 (47.9%) lower extremity injuries, and 5 (4.2%) concomitant upper and lower extremity injuries. The most common fracture patterns were ankle fractures (16, 11.7%), followed by tibial shaft (14, 10.2%), tibial plateau (13, 9.5%), and radial head fractures (11, 8.0%). There was a 33.3% incidence of associated posterior malleolar fractures in the spiral tibial shaft fractures, 31.0% of posterior malleolar involvement and 18.8% of isolated vertical medial malleolar fractures in the ankle fractures, and 61.5% of posterior comminution in the tibial plateau fractures.

conclusionE-scooter and e-bike related injuries have a high incidence of tibial shaft fractures, ankle fractures, tibial plateau fractures, and radial head fractures. There should be a high index of suspicion for posterior and medial involvement in lower extremity fractures sustained due to e-scooter or e-bikes. Identifying specific fracture patterns seen in e-scooter and e-bike related mechanisms will help guide management of these injuries.

Indexed as

Ankle FracturesRadial Head and Neck FracturesRadius FracturesTibial FracturesTibial Plateau FracturesAdolescentAdultFemaleHumansIncidenceMaleMiddle AgedRetrospective StudiesTrauma CentersYoung AdultAnkle fracturesElectric bikeElectric scooterTibial plateau fracturesTibial shaft

Identifiers

PMID36922264
PMCPMC10755772
OpenAlexW4321794086

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.