Evidence map›Paper›PMID 33743680›Full record

ArticleBMC medical education2021

Can ultrasound novices develop image acquisition skills after reviewing online ultrasound modules?

Elaine Situ-LaCasse, Josie Acuña, Dang Huynh, Richard Amini, Steven Irving, Kara Samsel, Asad E Patanwala, David E Biffar, Srikar Adhikari

Abstract read
In one paragraph

Article in BMC medical education, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Trial
  2. Review
  3. Article
  4. E-learning Challenges among Healthcare Students: A Scoping Review.Journal of advances in medical education & professionalism · 2026
    Review
  5. Article
  6. Article
  7. Observational
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Diagnostics (Basel, Switzerland) · 2022
    Article
  16. Article
  17. Ultrasound virtual skills based workshop: An African experience in the COVID era.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2022
    Article
  18. 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

9 authors.

Elaine Situ-LaCasseUniversity of Arizona, College of Medicine & Banner University Medical Center - Tucson, Department of Emergency Medicine, PO Box 245057, Tucson, AZ, 85724, USA. esitu@email.arizona.edu.ORCID http://orcid.org/0000-0002-2436-4410
Josie AcuñaUniversity of Arizona, College of Medicine & Banner University Medical Center - Tucson, Department of Emergency Medicine, PO Box 245057, Tucson, AZ, 85724, USA.
Dang HuynhTucson Medical Center, Department of Emergency Medicine, Tucson, AZ, USA.
Richard AminiCollege of Medicine, Department of Emergency Medicine, University of Arizona, Tucson, AZ, USA.
Steven IrvingMedical Center Hospital, Odessa, TX, USA.
Kara SamselDepartment of Emergency Medicine, Texas Tech University Health Sciences Center, El Paso, TX, USA.
Asad E PatanwalaUniversity of Sydney, Sydney School of Pharmacy, Sydney, Australia.
David E BiffarArizona Simulation Technology and Education Center - University of Arizona Health Sciences, Tucson, AZ, USA.
Srikar AdhikariCollege of Medicine, Department of Emergency Medicine, University of Arizona, Tucson, AZ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPoint-of-care ultrasound is becoming a ubiquitous diagnostic tool, and there has been increasing interest to teach novice practitioners. One of the challenges is the scarcity of qualified instructors, and with COVID-19, another challenge is the difficulty with social distancing between learners and educators. The purpose of our study was to determine if ultrasound-naïve operators can learn ultrasound techniques and develop the psychomotor skills to acquire ultrasound images after reviewing SonoSim® online modules.

methodsThis was a prospective study evaluating first-year medical students. Medical students were asked to complete four SonoSim® online modules (aorta/IVC, cardiac, renal, and superficial). They were subsequently asked to perform ultrasound examinations on standardized patients utilizing the learned techniques/skills in the online modules. Emergency Ultrasound-trained physicians evaluated medical students' sonographic skills in image acquisition quality, image acquisition difficulty, and overall performance. Data are presented as means and percentages with standard deviation. All P values are based on 2-tailed tests of significance.

resultsTotal of 44 medical students participated in the study. All (100%) students completed the hands-on skills evaluation with a median score of 83.7% (IQR 76.7-88.4%). Thirty-three medical students completed all the online modules and quizzes with median score of 87.5% (IQR 83.8-91.3%). There was a positive association between module quiz performance and the hands-on skills performance (R-squared = 0.45; p < 0.001). There was no statistically significant association between module performance and hands-on performance for any of the four categories individually. In all four categories, the evaluators' observation of the medical students' difficulty obtaining views correlated with hands-on performance scores.

conclusionsOur study findings suggest that ultrasound-naïve medical students can develop basic hands-on skills in image acquisition after reviewing online modules.

Indexed as

Clinical CompetencePoint-of-Care SystemsUltrasonographyCOVID-19Education, DistanceEducation, Medical, UndergraduateFemaleHumansMaleProspective StudiesSARS-CoV-2Asynchronous learningMedical student ultrasound educationPoint-of-care ultrasoundSimulationUltrasound education

Identifiers

PMID33743680
PMCPMC7980807

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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.