55. Simulation based training of technical surgical skills: A review of a five-year collaborative research program supported by the RCPSC Medical Education Funds
Bibliographic record
Abstract
During the past five years, with support from the RCPSC, a collaborative group of researchers conducted projects investigating issues related to simulation based training of technical surgical skills. The aim of this presentation is to review the body of work generated, its significance, and outline future research plans. In all studies, participants were medical students and residents from 3 medical schools in Ontario. First, we successfully demonstrated that trainees benefit from simulation-based practice by improving their ability to multitask. This ability not only increases technical proficiency, but also results in an enhanced ability to learn other aspects of surgery. Second, we showed that the adaptation of learning theories helps in optimizing training curricula by matching the fidelity of a simulator to the trainees’ level of expertise. Third, we provided validation of both expert and computer based methods for assessment. We showed that computer based assessments are sufficient for the evaluation of trainees learning fundamental skills, while expert based measures are more effective in the evaluation of performance on complex technical skills. Finally we demonstrated that examination-induced stress has a facilitating effect on trainees’ skills performance. This body of research lends support for the inclusion of a simulation based approach to training technical skills. It also highlights the importance of the choice of assessment methods. Collectively this work highlights the need for further research in the optimization of training methods by the incorporation of learning theory into the existing training curricula. Related to this, further research in our laboratory will investigate the effects of practice schedule and expert feedback, as well as the role of self-regulated practice in the acquisition of technical surgical skills. Xeroulis GJ, Park J, Moulton CA, Reznick RK, Leblanc V, Dubrowski A. Teaching suturing and knot-tying skills to medical students: a randomized controlled study comparing computer-based video instruction and (concurrent and summary) expert feedback. Surgery 2007; 141(4):442-9. Brydges R, Sidhu R, Park J, Dubrowski A. Construct validity of computer-assisted assessment: quantification of movement processes during a vascular anastomosis on a live porcine model. Am J Surg. 2007; 193(4):523-9. Brydges R, Carnahan H, Backstein D, Dubrowski A. Application of motor learning principles to complex surgical tasks: searching for the optimal practice schedule. J Mot Behav. 2007; 39(1):40-8.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".