Modeling Learning in Surgical Practice
Autor: | Thomas Edward Glover, Rajna Golubic, Epaminondas Markos Valsamis, Adnan Hussain, Amir-Reza Jenabzadeh, Henry Husband |
---|---|
Rok vydání: | 2017 |
Předmět: |
Male
Models Educational Databases Factual Process (engineering) Computer science Active learning (machine learning) Arthroplasty Replacement Hip High variability Operative Time Machine learning computer.software_genre Education 03 medical and health sciences 0302 clinical medicine Humans 030212 general & internal medicine Arthroplasty Replacement Knee Retrospective Studies Surgeons business.industry Imageless navigation Model complexity Surgery Computer-Assisted Learning curve 030220 oncology & carcinogenesis Unsupervised learning Operative time Surgery Female Artificial intelligence Clinical Competence business computer Learning Curve |
Zdroj: | Journal of surgical education. 75(1) |
ISSN: | 1878-7452 |
Popis: | Objective Methods that model surgical learning curves are frequently descriptive and lack the mathematical rigor required to extract robust, meaningful, and quantitative information. We aimed to formulate a method to model learning that is tailored to dealing with the high variability seen in surgical data and can readily extract important quantitative information such as learning rate, length of learning, and learnt level of performance. Methods We developed a method where progressively more complex models are fitted to learning data. These include novel models that split the learning data into 2 linear phases and fit adjoining lines using least squares regression. The models were compared and the least complex model was selected unless a more complex one was significantly better. Significance was tested by Fischer tests. We applied this method to total hip and knee replacements using imageless navigation, analyzing the operative time for a surgeon’s first 50 and 60 operations, respectively. This method was then tested against 4 sets of simulated learning data. Results The proposed method of progressive model complexity successfully modeled the learning curve among real operative data. It was also effective in deducing the underlying trends in simulated scenarios, created to represent typical situations that can practically arise in any learning process. Conclusions The novel modeling method can be used to extract meaningful and quantitative information from learning data displaying high variability seen in surgical practice. By using simple and intuitive models, the method is accessible to researchers and educators without the need for specialist statistical knowledge. |
Databáze: | OpenAIRE |
Externí odkaz: |
načítá se...