3D simulation of aneurysm clipping: Data analysis

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dc.contributor.authorMery F.
dc.contributor.authorMéndez-Orellana C.
dc.contributor.authorAranda F.
dc.contributor.authorRojas R.
dc.contributor.authorVillanueva P.
dc.contributor.authorMéndez-Orellana C.
dc.contributor.authorTorres J.
dc.contributor.authorCaro I.
dc.contributor.authorPesenti J.
dc.contributor.authorGermano I.
dc.date.accessioned2024-01-10T13:44:34Z
dc.date.available2024-01-10T13:44:34Z
dc.date.issued2021
dc.description.abstract© 2021Aneurysm clipping requires the proficiency of several skills, yet the traditional way of practicing them has been recently challenged. The use of simulators could be an alternative educational tool. The aim of this data analysis is to provide further evaluation of a reusable low-cost 3D printed training model we developed for aneurysm clipping [1]. The simulator was designed to replicate the bone structure, arteries and targeted aneurysms. Thirty-two neurosurgery residents performed a craniotomy and aneurysm clipping using the model and then filled out a survey. The survey was designed in two parts: a 5-point Likert scale questionnaire and three questions requiring written responses [1]. Two dimensions of the model were evaluated by the questionnaire: the face validity, assessed by 5 questions about the realism of the model, and the content validity, assessed by 6 questions regarding the usefulness of the model during the different steps of the training procedure. The three questions requiring written responses referred to the strengths and weaknesses of the simulator and a global yes/no question as to whether or not they would repeat the experience. Demographic data, experience level and survey responses of the residents were grouped in a dataset [2]. A descriptive analysis was performed for each dimension. Then, the groups were compared according to their level of expertise (Junior and Senior groups) with an independent sample t-test. A Confirmatory Factor Analysis (CFA) was estimated, using a Weighted Least Squares Mean Variance adjusted (WLSMV) which works best for the ordinal data [3]. Fitness was calculated using chi-square (χ2) test, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and the Root Mean Square Error of Approximation (RMSEA). A non-significant χ2, CFI and TLI greater than 0.90 and RMSEA < 0.08 were considered an acceptable fit [4]. All data analysis was performed using IBM SPSS 23.0 statistical software. Data are reported as mean + standard deviation (SD). A probability p < 0.05 was considered significant. Exploratory Factor Analysis was done to explore the factorial structure of the 11-items scale in the sample, first we performed a principal components analysis. The Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis (KMO = 0.784; Bartlett's Test of Sphericity χ2 (55) = 243.44, p < .001), indicating correlation is adequate for factor analysis. Considering Eigen values greater than 1, a two-factor solution explained 73.1% of the variance but left one item in factor 2 (Q 11). The results of this factor analysis are presented in Table 1. Confirmatory Factor Analysis, considering only the 10 items in the first factor (removing question 11 of our model), was performed. This model reached the following fit: χ2 (35) = 38.821, p > .05; CFI = 0.997; TLI = 0.996; RMSEA 0.058, without any error terms to exhibit covariance. Regarding the reliability of the questionnaire, the internal consistency was explored in the 10 items selected in the confirmatory factor analysis with an alpha coefficient (α = 0.941).
dc.fechaingreso.objetodigital2024-04-09
dc.fuente.origenScopus
dc.identifier.doi10.1016/j.dib.2021.107258
dc.identifier.eissn23523409
dc.identifier.issn23523409
dc.identifier.pubmedidMEDLINE:34277905
dc.identifier.scopusidSCOPUS_ID:85109133896
dc.identifier.urihttps://doi.org/10.1016/j.dib.2021.107258
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/78914
dc.identifier.wosidWOS:000689360000006
dc.information.autorucFacultad de Medicina; Mendez Orellana, Carolina Patricia; S/I; 1015155
dc.issue.numero107258
dc.language.isoen
dc.nota.accesocontenido completo
dc.publisherElsevier Inc.
dc.revistaData in Brief
dc.rightsacceso abierto
dc.subject3D printing
dc.subjectAneurysm clipping
dc.subjectData analysis
dc.subjectNeurosurgery education
dc.subjectSimulation
dc.subjectTraining model
dc.subject.ods04 Quality Education
dc.subject.odspa04 Educación y calidad
dc.title3D simulation of aneurysm clipping: Data analysis
dc.typeartículo
dc.volumen37
sipa.codpersvinculados1015155
sipa.indexWos
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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