Evidence map›Paper›PMID 33483780›Full record

ArticleAesthetic plastic surgery2021

Mind the Gap: a Competency-Based Scoping Review of Aesthetic and Reconstructive Reported Simulation Training Models.

Rodrigo Tejos, Juan Enrique Berner, Felipe Imigo, Nicolás Besser, Andrea Ramírez, Daniel Moreno, Gonzalo Yañez, Alvaro Cuadra, Susana Searle, Claudio Guerra

Abstract readScoping Review
PubMed Publisher
In one paragraph

Article in Aesthetic plastic surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Rodrigo TejosSection of Plastic and Reconstructive Surgery, Surgery Division, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, 8330077, Santiago, Chile.
Juan Enrique BernerPlastic Surgery Department, Royal Victoria Infirmary, Newcastle upon Tyne, UK.
Felipe ImigoServicio de Cirugía, Hospital de Puerto Montt, Región de los Lagos, Chile.
Nicolás BesserServicio de Cirugía, Hospital de Puerto Montt, Región de los Lagos, Chile.
Andrea RamírezSection of Plastic and Reconstructive Surgery, Surgery Division, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, 8330077, Santiago, Chile.
Daniel MorenoSection of Plastic and Reconstructive Surgery, Surgery Division, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, 8330077, Santiago, Chile.
Gonzalo YañezSection of Plastic and Reconstructive Surgery, Surgery Division, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, 8330077, Santiago, Chile.
Alvaro CuadraSection of Plastic and Reconstructive Surgery, Surgery Division, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, 8330077, Santiago, Chile.
Susana SearleSection of Plastic and Reconstructive Surgery, Surgery Division, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, 8330077, Santiago, Chile.
Claudio GuerraSection of Plastic and Reconstructive Surgery, Surgery Division, School of Medicine, Pontificia Universidad Católica de Chile, Diagonal Paraguay 362, 8330077, Santiago, Chile. plasticsurgeryuc3@uc.cl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSimulation training has become an integral part of plastic surgery postgraduate curricula. It facilitates the acquisition of skills in a safe environment that can be later transferred to real-life settings. A variety of models have been described covering some aspects of the specialty better than others. The aim of this study was to identify and classify all the previously reported plastic surgery simulation models and the possible gaps having the Accreditation Council for Graduate Medical Education (ACGME) list of competencies as a guide.

methodsThrough a Delphi process, the complete list of ACGME minimum requirements for certification was analyzed to identify domains amenable for simulation training. A systematic search was conducted in Pubmed looking for all previously reported simulation models in plastic surgery. Predefined inclusion and exclusion criteria and parallel blind review were used to identify eligible models.

resultsA total of 81 ACGME competencies were identified. Following a 3-round Delphi process, consensus was reached on 19 reconstructive and 15 aesthetic surgery domains suitable for simulation training. 1667 articles were initially retrieved from Pubmed, of which 66 articles were eligible for inclusion. Descriptive (65%), quasi-experimental (24%) and experimental studies (11%) were found. For the 34 identified ACGME competencies, there were simulation models described for 58.8% of these, mostly covering reconstructive surgery (84.2%) while for aesthetic surgery it was 13.3%.

conclusionsThis scoping review has identified that there are still gaps in ACGME competencies that could benefit from new simulation training models, especially in those related to aesthetic surgery. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Indexed as

Plastic Surgery ProceduresSimulation TrainingSurgery, PlasticEducation, Medical, GraduateEstheticsHumans

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.