Evaluation of a Tensiometer to Provide Objective Feedback in Knot-Tying Performance

Abstract

Training and assessment methods for knot tying by medical students or residents have traditionally been subjective. Objective methods for evaluating creation of a tied knot should include assessing the strength and quality of the knotted suture. The purpose of this study was to evaluate the use of a tensiometer as a feedback device for improving knot-tying performance. Twelve medical students with no knot-tying experience were selected to perform three-throw instrument ties with 00 silk suture. Students were randomly assigned to perform between 10 and 20 baseline knots and then received one of four feedback training conditions followed by 10 completion knots. Subjects were timed, and all knots were pulled in the tensiometer to assess for strength and slippage. Differences between baseline and completed knots for each subject were analyzed with an unpaired t test. Subjects receiving both subjective and tensiometer feedback demonstrated the greatest improvements in knot quality score (KQS) and slip percentage (Subject 1: 0.15 ± 0.9 vs 0.21 ± 0.05, P < 0.04, 75% vs 60%, P = NS; Subject 2: 0.22 ± 0.10 vs 0.29 ± 0.05, P < 0.02, 33% vs 0%, P < 0.05; Subject 3: 0.10 ± 0.07 vs 0.25 ± 0.07, P < 0.0001, 60% vs 10%, P < 0.01). Objective assessment of knot-tying performance is possible using the tensiometer device. Introduction of the tensiometer during the learning phase produced improved KQS and slip percentage in most students regardless of the number of baseline knots tied. Greatest improvements in performance were seen when the tensiometer was used in combination with subjective instruction.

Popularity of suture materials among residents and faculty members of a postdoctoral periodontology program

“…The selection of the suture material is based on a number of factors involving desirable longevity, anatomical location of the suture site, and the nature and mechanical properties of the tissues being sutured . Knot strength is also a major factor in suture selection, as it will enhance the security of the knot and maintain the desirable wound tension . In dentistry the existence of saliva, bacterial byproducts, and plaque accumulation affects the dimensional stability and mechanical properties of the suture material, compared to dry and isolated wounds of the body .…”

Barbed Suture for Gastrointestinal Closure: A Randomized Control Trial

“…Biomechanical testing of laparoscopic suturing demonstrated that roughly 50% of the suture failures resulted from slipping or unraveling of the knots. 2,3 Efforts have been made to improve…”

Expert-Based Speed-Precision Control in Early Simulator Training for Novice Surgeons

“…It is based on previously validated cognitive models of human performance learning showing that the individual speed-precision strategies of novices, which occur spontaneously and unconsciously [2,22,24,25], can seriously compromise precision learning at all further stages of training if they are not controlled and corrected for as early as possible in the process. In robot-assisted surgical procedures, for example, where the camera moves along with the tool, for example [10][11][12][13][14][15][16] Peer-reviewed version available at Information 2018, 9, 316; doi:10.3390/info9120316 decision model for unsupervised training procedures of the kind proposed here in this article could also be adapted to such performance criteria on the basis of device-specific expert performance benchmark data.…”

“…Benchmarking individual levels of proficiency on the performance levels of experts on a validated metric-based simulation system has well-established intrinsic face validity [9] and appears a better approach than benchmarking on some abstract performance concept based on expert consensus, for example. Building expert performance as a basis for skill assessment into simulator training programs ensures that the "pass" level is defined on realistic criteria set directly by the proficiency levels of individuals who are actually very experienced at performing the actual clinical procedure [9][10][11][12][13]. Artificial intelligence provides well-suited concepts for reinforcement learning procedures and allows building prior benchmark knowledge on expert performance into simulator training most effectively.…”