Objective classification of residents based on their psychomotor laparoscopic skills.
BACKGROUND
From the clinical point of view, it is important to recognize residents' level of expertise with regard to basic psychomotor skills. For that reason, surgeons and surgical organizations (e.g., Acreditation Council for Graduate Medical Education, ACGME) are calling for assessment tools that credential residents as technically competent. Currently, no method is universally accepted or recommended for classifying residents as "experienced," "intermediates," or "novices" according to their technical abilities. This study introduces a classification method for recognizing residents' level of experience in laparoscopic surgery based on psychomotor laparoscopic skills alone.


METHODS
For this study, 10 experienced residents (>100 laparoscopic procedures performed), 10 intermediates (10-100 procedures performed), and 11 novices (no experience) performed four tasks in a box trainer. The movements of the laparoscopic instruments were recorded with the TrEndo tracking system and analyzed using six motion analysis parameters (MAPs). The MAPs of all participants were submitted to principal component analysis (PCA), a data reduction technique. The scores of the first principal components were used to perform linear discriminant analysis (LDA), a classification method. Performance of the LDA was examined using a leave-one-out cross-validation.


RESULTS
Of 31 participants, 23 were classified correctly with the proposed method, with 7 categorized as experienced, 7 as intermediates, and 9 as novices.


CONCLUSIONS
The proposed method provides a means to classify residents objectively as experienced, intermediate, or novice surgeons according to their basic laparoscopic skills. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers.
Chmarra
Magdalena K
MK
Department of BioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands. m.k.chmarra@tudelft.nl
Klein
Stefan
S
de Winter
Joost C F
JC
Jansen
Frank-Willem
FW
Dankelman
Jenny
J
D002983:Clinical Competence; D005762:Gastroenterology / Q000193:education*; D006801:Humans; D007396:Internship and Residency / Q000145:classification*; D020706:Laparoscopes; D010535:Laparoscopy / Q000523:psychology* / Q000592:standards; D009068:Movement / Q000502:physiology*; D013647:Task Performance and Analysis