Changing attitudes and improving skills: demonstrating the value of the SAGES flexible endoscopy course for fellows

Gardner, Aimee K.; Marks, Jeffrey M.; Pauli, Eric M.; Majumder, Arnab; Dunkin, Brian J.

doi:10.1007/s00464-016-4944-4

Cited by 8 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our study provides a quantitative understanding that individuals with different initial skill levels require different practice to reach a final performance level. Differences in learning characteristics may be due to innate factors including handedness, gender, visual-spatial ability, and confidence level [45][46][47][48][49][50][51] or extrinsic factors including research experience, selection of specialty, and grouping 49,52,53 .…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach to predict surgical learning curves

Gao

Krüger

Intes

et al. 2020

Surgery

View full text Add to dashboard Cite

Background: Contemporary surgical training programs rely on the repetition of selected surgical motor tasks. Such methodology is inherently open-ended with no control on the time taken to attain a set level of proficiency, given the trainees' intrinsic differences in initial skill levels and learning abilities. Hence, an efficient training program should aim at tailoring the surgical training protocols to each trainee. In this regard, a predictive model utilizing information from the initial learning stage to predict learning curve characteristics should facilitate the whole surgical training process.Methods: This paper analyzes learning curve data to train a multivariate supervised machine learning model. One factor is extracted to define the trainees' learning ability. An unsupervised machine learning model is also utilized for trainee classification. When established, the model can predict robustly the learning curve characteristics based on the first few trials. Results:We show that the information present in the first ten trials of surgical tasks can be utilized to predict the number of trials required to achieve proficiency (R 2 = 0.72) and the final performance level (R 2 = 0.89). Furthermore, only a single factor, learning index, is required to describe the learning process and to classify learners with unique learning characteristics. Conclusions:Using machine learning models, we show, for the first time, that the first few trials contain sufficient information to predict learning curve characteristics and that a single factor can capture the complex learning behavior. Using such models holds the potential for personalization of training regimens, leading to greater efficiency and lower costs.

show abstract