Evidence map›Paper›PMID 33298042›Full record

ArticleBMC medical education2020

Simulation training for emergency skills: effects on ICU fellows' performance and supervision levels.

Bjoern Zante, Joerg C Schefold

Abstract read
In one paragraph

Article in BMC medical education, 2020. 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.

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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

2 authors.

Bjoern ZanteDepartment of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 10, 3010, Bern, Switzerland. bjoern.zante@insel.ch.ORCID http://orcid.org/0000-0002-1161-9648
Joerg C SchefoldDepartment of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 10, 3010, Bern, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe application of manual emergency skills is essential in intensive care medicine. Simulation training on cadavers may be beneficial. The aim of this study was to analyze a skill-training aiming to enhance ICU-fellows´ performance.

methodsA skill-training was prepared for chest tube insertion, pericardiocentesis, and cricothyroidotomy. Supervision levels (SL) for entrustable professional activities (EPA) were applied to evaluate skill performance. Pre- and post-training, SL and fellows´ self- versus consultants´ external assessment was compared. Time on skill training was compared to conventional training in the ICU-setting.

resultsComparison of pre/post external assessment showed reduced required SL for chest tube insertion, pericardiocentesis, and cricothyroidotomy. Self- and external assessed SL did not significantly correlate for pre-training/post-training pericardiocentesis and post-training cricothyroidotomy. Correlations were observed for self- and external assessment SL for chest tube insertion and pre-assessment for cricothyroidotomy. Compared to conventional training in the ICU-setting, chest tube insertion training may further be time-saving.

conclusionsEmergency skill training separated from a daily clinical ICU-setting appeared feasible and useful to enhance skill performance in ICU fellows and may reduce respective SL. We observed that in dedicated skill-training sessions, required time resources would be somewhat reduced compared to conventional training methods.

Indexed as

Clinical CompetenceSimulation TrainingCadaverChest TubesHumansIntensive Care UnitsAssessmentChest tubeCost effectiveCricoconiotomyCritical careEducationEPALearningPericardiocentesisSelf perception

Identifiers

PMID33298042
PMCPMC7726897

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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.