Epaminondas Markos Valsamis1, Rajna Golubic2, Thomas E Glover3, Henry Husband4, Adnan Hussain5, Amir-Reza Jenabzadeh5. 1. Cardiology Department, Papworth Hospital, Papworth Everard, Cambridge, United Kingdom. Electronic address: markosvalsamis@gmail.com. 2. Cardiology Department, Papworth Hospital, Papworth Everard, Cambridge, United Kingdom. 3. Addenbrookes Hospital, Cambridge, United Kingdom. 4. Faculty of Mathematics, University of Cambridge, Cambridge, United Kingdom. 5. Trauma and Orthopedics Department, Hinchingbrooke Hospital, Huntingdon, United Kingdom.
Abstract
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.
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.
Authors: Hugo Layard Horsfall; Zeqian Mao; Chan Hee Koh; Danyal Z Khan; William Muirhead; Danail Stoyanov; Hani J Marcus Journal: Front Surg Date: 2022-06-06