Evidence map›Paper›PMID 35385532›Full record

ArticlePloS one2022

E-scooter related injuries: Using natural language processing to rapidly search 36 million medical notes.

Kimon L H Ioannides, Pin-Chieh Wang, Kamran Kowsari, Vu Vu, Noah Kojima, Dayna Clayton, Charles Liu, Tarak K Trivedi, David L Schriger, Joann G Elmore

Abstract read
In one paragraph

Article in PloS one, 2022. 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
–field-weighted citation impact
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.

  1. Maxillofacial fractures associated with electric scooter trauma: a cohort study from a national level I trauma center.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2026
    Observational
  2. Article
  3. Observational
  4. Unconventional mobility, conventional trauma: a comparative analysis of e-scooter-related fractures.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2024
    Article
  5. Observational
  6. Using e-scooters: An easy way to get home or a nightmare? An orthopedic perspective on e-scooter accidents.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2023
    Article
  7. Article
  8. Article
  9. Article
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

10 authors.

Kimon L H IoannidesDepartment of Emergency Medicine, University of California, San Francisco-Fresno Medical Education Program, Fresno, CA, United States of America.ORCID 0000-0002-6384-2235
Pin-Chieh WangDepartment of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America.
Kamran KowsariOffice of Health Informatics and Analytics, UCLA Health, University of California, Los Angeles, CA, United States of America.ORCID 0000-0002-6451-4786
Vu VuOffice of Health Informatics and Analytics, UCLA Health, University of California, Los Angeles, CA, United States of America.
Noah KojimaDepartment of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America.ORCID 0000-0002-3667-9719
Dayna ClaytonDepartment of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America.
Charles LiuNational Clinician Scholars Program, University of California, Los Angeles, CA, United States of America.
Tarak K TrivediDepartment of Emergency Medicine, University of California, Los Angeles, CA, United States of America.
David L SchrigerDepartment of Emergency Medicine, University of California, Los Angeles, CA, United States of America.
Joann G ElmoreNational Clinician Scholars Program, University of California, Los Angeles, CA, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundShareable e-scooters have become popular, but injuries to riders and bystanders have not been well characterized. The goal of this study was to describe e-scooter injuries and estimate the rate of injury per e-scooter trip. METHODS AND

findingsRetrospective review of patients presenting to 180 clinics and 2 hospitals in greater Los Angeles between January 1, 2014 and May 14, 2020. Injuries were identified using a natural language processing (NLP) algorithm not previously used to identify injuries, tallied, and described along with required healthcare resources. We combine these tallies with municipal data on scooter use to report a monthly utilization-corrected rate of e-scooter injuries. We searched 36 million clinical notes. Our NLP algorithm correctly classified 92% of notes in the testing set compared with the gold standard of investigator review. In total, we identified 1,354 people injured by e-scooters; 30% were seen in more than one clinical setting (e.g., emergency department and a follow-up outpatient visit), 29% required advanced imaging, 6% required inpatient admission, and 2 died. We estimate 115 injuries per million e-scooter trips were treated in our health system.

conclusionsOur observed e-scooter injury rate is likely an underestimate, but is similar to that previously reported for motorcycles. However, the comparative severity of injuries is unknown. Our methodology may prove useful to study other clinical conditions not identifiable by existing diagnostic systems.

Indexed as

Accidents, TrafficNatural Language ProcessingEmergency Service, HospitalHumansMotorcyclesRetrospective Studies

Identifiers

PMID35385532
PMCPMC8985928

What OpenQuestion holds

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Registered trials

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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.