Evidence map›Paper›PMID 31723483›Full record

ArticleCureus2019

Point-of-care Ultrasound Training During an Emergency Medicine Clerkship: A Prospective Study.

Arif Alper Cevik, Elif Dilek Cakal, Fikri Abu-Zidan

Abstract read
In one paragraph

Article in Cureus, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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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

3 authors.

Arif Alper CevikInternal Medicine, Emergency Medicine Section, United Arab Emirates University, College of Medicine and Health Sciences, Al Ain, ARE.
Elif Dilek CakalEmergency Medicine, Mersin City Education and Research Hospital, Mersin, TUR.
Fikri Abu-ZidanSurgery, United Arab Emirates University, Al Ain, ARE.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim This study evaluated the effects of three-hour instructor-led training courses in the Extended Focused Assessment Sonography for Trauma (EFAST) and Rapid Ultrasound in Shock and Hypotension (RUSH) protocols on knowledge gain and retention by final-year medical students. Methods This prospective study evaluated 79 final year medical students participating in an emergency medicine (EM) clerkship during the 2017-2018 academic year. None of the included students had prior formal ultrasound training or hands-on experience. All students participated in three-hour training courses on the EFAST and RUSH protocols, with training on each protocol involving one hour of didactic training and two hours of practical training. Knowledge improvement was measured by testing before and after each course, and knowledge retention was evaluated on a final clerkship multiple choice question (MCQ) examination. Results Median scores were significantly higher after rather than before both the EFAST (15; range, 12-19 vs. 7; range, 2-18; p < 0.0001) and RUSH (16; range, 6-20 vs. 6; range, 1-13; p < 0.0001) courses. EFAST knowledge was significantly higher than RUSH knowledge before (p = 0.04) but not after (p = 0.82) taking the respective course. The RUSH score was significantly lower than the EFAST score on the final clerkship MCQ examination (p < 0.0001). Conclusions Three hours of instructor-led ultrasound training given during an EM clerkship significantly increased knowledge of both the EFAST and RUSH protocols. Knowledge retention after two weeks was higher for the EFAST than the RUSH protocol. A longer period of RUSH training may improve the retention of knowledge.

Indexed as

efastemergency medicinemedical studentpocusrushtrainingultrasoundundergraduate

Identifiers

PMID31723483
PMCPMC6844539

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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.