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Preliminary Face and Construct Validation Study of a Virtual Basic Laparoscopic Skill Trainer

Identifieur interne : 001D88 ( Pmc/Checkpoint ); précédent : 001D87; suivant : 001D89

Preliminary Face and Construct Validation Study of a Virtual Basic Laparoscopic Skill Trainer

Auteurs : Ganesh Sankaranarayanan ; Henry Lin ; Venkata S. Arikatla ; Maureen Mulcare ; Likun Zhang ; Alexandre Derevianko ; Robert Lim ; David Fobert ; Caroline Cao ; Steven D. Schwaitzberg ; Daniel B. Jones ; Suvranu De

Source :

RBID : PMC:3123729

Abstract

AbstractBackground

The Virtual Basic Laparoscopic Skill Trainer (VBLaST) is a developing virtual-reality–based surgical skill training system that incorporates several of the tasks of the Fundamentals of Laparoscopic Surgery (FLS) training system. This study aimed to evaluate the face and construct validity of the VBLaST™ system.

Materials and Methods

Thirty-nine subjects were voluntarily recruited at the Beth Israel Deaconess Medical Center (Boston, MA) and classified into two groups: experts (PGY 5, fellow and practicing surgeons) and novice (PGY 1–4). They were then asked to perform three FLS tasks, consisting of peg transfer, pattern cutting, and endoloop, on both the VBLaST and FLS systems. The VBLaST performance scores were automatically computed, while the FLS scores were rated by a trained evaluator. Face validity was assessed using a 5-point Likert scale, varying from not realistic/useful (1) to very realistic/useful (5).

Results

Face-validity scores showed that the VBLaST system was significantly realistic in portraying the three FLS tasks (3.95 ± 0.909), as well as the reality in trocar placement and tool movements (3.67 ± 0.874). Construct-validity results show that VBLaST was able to differentiate between the expert and novice group (P = 0.015). However, of the two tasks used for evaluating VBLaST, only the peg-transfer task showed a significant difference between the expert and novice groups (P = 0.003). Spearman correlation coefficient analysis between the two scores showed significant correlation for the peg-transfer task (Spearman coefficient 0.364; P = 0.023).

Conclusions

VBLaST demonstrated significant face and construct validity. A further set of studies, involving improvement to the current VBLaST system, is needed to thoroughly demonstrate face and construct validity for all the tasks.


Url:
DOI: 10.1089/lap.2009.0030
PubMed: 20201683
PubMed Central: 3123729


Affiliations:


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PMC:3123729

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<title>Abstract</title>
<sec>
<title>Background</title>
<p>The Virtual Basic Laparoscopic Skill Trainer (VBLaST
<sup></sup>
) is a developing virtual-reality–based surgical skill training system that incorporates several of the tasks of the Fundamentals of Laparoscopic Surgery (FLS) training system. This study aimed to evaluate the face and construct validity of the VBLaST™ system.</p>
</sec>
<sec>
<title>Materials and Methods</title>
<p>Thirty-nine subjects were voluntarily recruited at the Beth Israel Deaconess Medical Center (Boston, MA) and classified into two groups: experts (PGY 5, fellow and practicing surgeons) and novice (PGY 1–4). They were then asked to perform three FLS tasks, consisting of peg transfer, pattern cutting, and endoloop, on both the VBLaST and FLS systems. The VBLaST performance scores were automatically computed, while the FLS scores were rated by a trained evaluator. Face validity was assessed using a 5-point Likert scale, varying from not realistic/useful (1) to very realistic/useful (5).</p>
</sec>
<sec>
<title>Results</title>
<p>Face-validity scores showed that the VBLaST system was significantly realistic in portraying the three FLS tasks (3.95 ± 0.909), as well as the reality in trocar placement and tool movements (3.67 ± 0.874). Construct-validity results show that VBLaST was able to differentiate between the expert and novice group (
<italic>P</italic>
 = 0.015). However, of the two tasks used for evaluating VBLaST, only the peg-transfer task showed a significant difference between the expert and novice groups (
<italic>P</italic>
 = 0.003). Spearman correlation coefficient analysis between the two scores showed significant correlation for the peg-transfer task (Spearman coefficient 0.364;
<italic>P</italic>
 = 0.023).</p>
</sec>
<sec>
<title>Conclusions</title>
<p>VBLaST demonstrated significant face and construct validity. A further set of studies, involving improvement to the current VBLaST system, is needed to thoroughly demonstrate face and construct validity for all the tasks.</p>
</sec>
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</front>
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<journal-id journal-id-type="nlm-ta">J Laparoendosc Adv Surg Tech A</journal-id>
<journal-id journal-id-type="publisher-id">lap</journal-id>
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<journal-title>Journal of Laparoendoscopic & Advanced Surgical Techniques. Part A</journal-title>
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<issn pub-type="ppub">1092-6429</issn>
<issn pub-type="epub">1557-9034</issn>
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<publisher-loc>140 Huguenot Street, 3rd FloorNew Rochelle, NY 10801USA</publisher-loc>
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<article-id pub-id-type="pmid">20201683</article-id>
<article-id pub-id-type="pmc">3123729</article-id>
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<article-categories>
<subj-group subj-group-type="heading">
<subject>Technical Reports</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Preliminary Face and Construct Validation Study of a Virtual Basic Laparoscopic Skill Trainer</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Sankaranarayanan</surname>
<given-names>Ganesh</given-names>
</name>
<degrees>PhD</degrees>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Lin</surname>
<given-names>Henry</given-names>
</name>
<degrees>MD</degrees>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Arikatla</surname>
<given-names>Venkata S.</given-names>
</name>
<degrees>MS</degrees>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Mulcare</surname>
<given-names>Maureen</given-names>
</name>
<degrees>BS</degrees>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Likun</given-names>
</name>
<degrees>MsC</degrees>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Derevianko</surname>
<given-names>Alexandre</given-names>
</name>
<degrees>MD</degrees>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Lim</surname>
<given-names>Robert</given-names>
</name>
<degrees>MD</degrees>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Fobert</surname>
<given-names>David</given-names>
</name>
<degrees>BS</degrees>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cao</surname>
<given-names>Caroline</given-names>
</name>
<degrees>PhD</degrees>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Schwaitzberg</surname>
<given-names>Steven D.</given-names>
</name>
<degrees>MD</degrees>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Jones</surname>
<given-names>Daniel B.</given-names>
</name>
<degrees>MD, MS</degrees>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>De</surname>
<given-names>Suvranu</given-names>
</name>
<degrees>ScD</degrees>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<aff id="aff1">
<label>
<sup>1</sup>
</label>
Department of Mechanical, Aerospace, and Nuclear Engineering,
<institution>Rensselaer Polytechnic Institute</institution>
, Troy, New York.</aff>
<aff id="aff2">
<label>
<sup>2</sup>
</label>
Department of Surgery, Beth Israel Deaconess Medical Center,
<institution>Harvard Medical School</institution>
, Boston, Massachusetts.</aff>
<aff id="aff3">
<label>
<sup>3</sup>
</label>
Department of Mechanical Engineering,
<institution>Tufts University</institution>
, Medford, Massachusetts.</aff>
<aff id="aff4">
<label>
<sup>4</sup>
</label>
Carl J. Shapiro Simulation and Skills Center,
<institution>Beth Israel Deaconess Medical Center</institution>
, Boston, Massachusetts.</aff>
<aff id="aff5">
<label>
<sup>5</sup>
</label>
<institution>Cambridge Health Alliance</institution>
, Cambridge, Massachusetts.</aff>
</contrib-group>
<author-notes>
<corresp>Address correspondence to:
<italic>Suvranu De, ScD, Department of Mechanical, Aerospace, and Nuclear Engineering, JEC 2046, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180. E-mail:</italic>
<email xlink:href="mailto:des@rpi.edu">des@rpi.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="ppub">
<month>3</month>
<year>2010</year>
<pmc-comment>string-date: March 2010</pmc-comment>
</pub-date>
<volume>20</volume>
<issue>2</issue>
<fpage>153</fpage>
<lpage>157</lpage>
<permissions>
<copyright-statement>Copyright 2010, Mary Ann Liebert, Inc.</copyright-statement>
<copyright-year>2010</copyright-year>
</permissions>
<self-uri xlink:type="simple" xlink:href="lap.2009.0030.pdf"></self-uri>
<abstract>
<title>Abstract</title>
<sec>
<title>Background</title>
<p>The Virtual Basic Laparoscopic Skill Trainer (VBLaST
<sup></sup>
) is a developing virtual-reality–based surgical skill training system that incorporates several of the tasks of the Fundamentals of Laparoscopic Surgery (FLS) training system. This study aimed to evaluate the face and construct validity of the VBLaST™ system.</p>
</sec>
<sec>
<title>Materials and Methods</title>
<p>Thirty-nine subjects were voluntarily recruited at the Beth Israel Deaconess Medical Center (Boston, MA) and classified into two groups: experts (PGY 5, fellow and practicing surgeons) and novice (PGY 1–4). They were then asked to perform three FLS tasks, consisting of peg transfer, pattern cutting, and endoloop, on both the VBLaST and FLS systems. The VBLaST performance scores were automatically computed, while the FLS scores were rated by a trained evaluator. Face validity was assessed using a 5-point Likert scale, varying from not realistic/useful (1) to very realistic/useful (5).</p>
</sec>
<sec>
<title>Results</title>
<p>Face-validity scores showed that the VBLaST system was significantly realistic in portraying the three FLS tasks (3.95 ± 0.909), as well as the reality in trocar placement and tool movements (3.67 ± 0.874). Construct-validity results show that VBLaST was able to differentiate between the expert and novice group (
<italic>P</italic>
 = 0.015). However, of the two tasks used for evaluating VBLaST, only the peg-transfer task showed a significant difference between the expert and novice groups (
<italic>P</italic>
 = 0.003). Spearman correlation coefficient analysis between the two scores showed significant correlation for the peg-transfer task (Spearman coefficient 0.364;
<italic>P</italic>
 = 0.023).</p>
</sec>
<sec>
<title>Conclusions</title>
<p>VBLaST demonstrated significant face and construct validity. A further set of studies, involving improvement to the current VBLaST system, is needed to thoroughly demonstrate face and construct validity for all the tasks.</p>
</sec>
</abstract>
<counts>
<fig-count count="3"></fig-count>
<table-count count="2"></table-count>
<ref-count count="22"></ref-count>
<page-count count="5"></page-count>
</counts>
</article-meta>
</front>
</pmc>
<affiliations>
<list></list>
<tree>
<noCountry>
<name sortKey="Arikatla, Venkata S" sort="Arikatla, Venkata S" uniqKey="Arikatla V" first="Venkata S." last="Arikatla">Venkata S. Arikatla</name>
<name sortKey="Cao, Caroline" sort="Cao, Caroline" uniqKey="Cao C" first="Caroline" last="Cao">Caroline Cao</name>
<name sortKey="De, Suvranu" sort="De, Suvranu" uniqKey="De S" first="Suvranu" last="De">Suvranu De</name>
<name sortKey="Derevianko, Alexandre" sort="Derevianko, Alexandre" uniqKey="Derevianko A" first="Alexandre" last="Derevianko">Alexandre Derevianko</name>
<name sortKey="Fobert, David" sort="Fobert, David" uniqKey="Fobert D" first="David" last="Fobert">David Fobert</name>
<name sortKey="Jones, Daniel B" sort="Jones, Daniel B" uniqKey="Jones D" first="Daniel B." last="Jones">Daniel B. Jones</name>
<name sortKey="Lim, Robert" sort="Lim, Robert" uniqKey="Lim R" first="Robert" last="Lim">Robert Lim</name>
<name sortKey="Lin, Henry" sort="Lin, Henry" uniqKey="Lin H" first="Henry" last="Lin">Henry Lin</name>
<name sortKey="Mulcare, Maureen" sort="Mulcare, Maureen" uniqKey="Mulcare M" first="Maureen" last="Mulcare">Maureen Mulcare</name>
<name sortKey="Sankaranarayanan, Ganesh" sort="Sankaranarayanan, Ganesh" uniqKey="Sankaranarayanan G" first="Ganesh" last="Sankaranarayanan">Ganesh Sankaranarayanan</name>
<name sortKey="Schwaitzberg, Steven D" sort="Schwaitzberg, Steven D" uniqKey="Schwaitzberg S" first="Steven D." last="Schwaitzberg">Steven D. Schwaitzberg</name>
<name sortKey="Zhang, Likun" sort="Zhang, Likun" uniqKey="Zhang L" first="Likun" last="Zhang">Likun Zhang</name>
</noCountry>
</tree>
</affiliations>
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