Evidence map›Paper›PMID 38822920›Full record

ArticleIrish journal of medical science2024

E-scooter-related imaging: a new emerging high-energy trauma patient.

Bryan Buckley, Pat Rohan, Holly Keating, Francis Delaney, Ivan Welaratne, Ferdia Bolster

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Article in Irish journal of medical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
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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

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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

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Bryan BuckleyMater Misericordiae University Hospital, Dublin, Ireland. buckleb@tcd.ie.ORCID http://orcid.org/0000-0002-3160-4232
Pat RohanMater Misericordiae University Hospital, Dublin, Ireland.
Holly KeatingMater Misericordiae University Hospital, Dublin, Ireland.
Francis DelaneyMater Misericordiae University Hospital, Dublin, Ireland.
Ivan WelaratneMater Misericordiae University Hospital, Dublin, Ireland.
Ferdia BolsterMater Misericordiae University Hospital, Dublin, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo quantify the volume and nature of radiological imaging performed for e-scooter related trauma presentations in a tertiary referral centre and to identify common radiological pattern of injuries.

methodsOur study received institutional review board approval. We retrospectively reviewed all anonymised radiological imaging studies performed in our institution for the term 'scooter' or 'e-scooter' between July 2020 and July 2021. The patient demographics, number of imaging studies performed and modality type as well as the nature of injuries identified were recorded.

resultsWithin the study period, 147 patients with e-scooter-related injuries were referred for radiological imaging. Forty-two (29%) of those patients had positive radiological findings. The vast majority of injuries on radiographs were upper limb injuries typical of a fall on outstretched hand (FOOSH) pattern. Thirty-two patients (22%) required advanced diagnostics with CT or MRI with 11 of these patients having positive findings. Four patients suffered significant head injuries including skull fractures and/or intracranial haemorrhage. Five patients suffered facial bone fractures, and 2 suffered spinal fractures. One patient suffered a handlebar abdominal trauma with a resulting large bowel injury ultimately requiring bowel resection.

conclusionE-scooter-related injuries represent a new and emerging high-energy trauma patient. These patients demonstrate injury patterns similar to other high-energy trauma such as road traffic accidents. Although the most common injuries are musculoskeletal upper limb injuries typical of FOOSH, a large proportion of these require advanced diagnostic imaging (CT, MRI) which were commonly positive for significant injuries.

Indexed as

Magnetic Resonance ImagingAccidental FallsAdolescentAdultAgedCraniocerebral TraumaFemaleFractures, BoneHumansMaleMiddle AgedRetrospective StudiesSkull FracturesSpinal FracturesTomography, X-Ray ComputedYoung AdultCTE-scooterMRITrauma

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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.