Training to proficiency in surgery using simulation: is there a moral obligation?

A deontological approach to surgical ethics advocates that patients have the right to receive the best care that can be provided. The ‘learning curve’ in surgical skill is an observable and measurable phenomenon. Surgical training may therefore carry risk to patients. This can occur directly, through inadvertent harm, or indirectly through theatre inefficiency and associated costs. Trainee surgeon operating, however, is necessary from a utilitarian perspective, with potential risk balanced by the greater societal need to train future independent surgeons.New technology means that the surgical learning curve could take place, at least in part, outside of the operating theatre. Simulation-based deliberate practice could be used to obtain a predetermined level of proficiency in a safe environment, followed by simulation-based assessment of operative competence. Such an approach would require an overhaul of the current training paradigm and significant investment in simulator technology. This may increasingly be viewed as necessary in light of well-discussed pressures on surgical trainees and trainers.This article discusses the obligations to trainees, trainers and training bodies raised by simulation technology, and outlines the current arguments both against and in favour of a simulation-based training-to-proficiency model in surgery. The significant changes to the current training paradigm that would be required to implement such a model are also discussed.

Potential Benefits of a Minimal Dose Eccentric Resistance Training Paradigm to Combat Sarcopenia and Age-Related Muscle and Physical Function Deficits in Older Adults

The ability of older adults to perform activities of daily living is often limited by the ability to generate high mechanical outputs. Therefore, assessing and developing maximal neuromuscular capacity is essential for determining age-related risk for functional decline as well as the effectiveness of therapeutic interventions. Interventions designed to enhance neuromuscular capacities underpinning maximal mechanical outputs could positively impact functional performance in daily life. Unfortunately, < 10% of older adults meet the current resistance training guidelines. It has recently been proposed that a more “minimal dose” RT model may help engage a greater proportion of older adults, so that they may realize the benefits of RT. Eccentric exercise offers some promising qualities for such an approach due to its efficiency in overloading contractions that can induce substantial neuromuscular adaptations. When used in a minimal dose RT paradigm, eccentric-based RT may be a particularly promising approach for older adults that can efficiently improve muscle mass, strength, and functional performance. One approach that may lead to improved neuromuscular function capacities and overall health is through heightened exercise tolerance which would favor greater exercise participation in older adult populations. Therefore, our perspective article will discuss the implications of using a minimal dose, submaximal (i.e., low intensity) multi-joint eccentric resistance training paradigm as a potentially effective, and yet currently underutilized, means to efficiently improve neuromuscular capacities and function for older adults.

HybridDTA: Hybrid Data Fusion through Pairwise Training for Drug-Target Affinity Prediction

Estimating drug-target binding affinity (DTA) is crucial for various tasks, including drug design, drug repurposing, and lead optimization. Advanced works adopt machine learning techniques, especially deep learning, to DTA estimation by utilizing the existing assay data. These powerful techniques make it possible to screen a massive amount of potential drugs with limited computation cost. However, a typical DNN-based training paradigm directly minimizes the distances between the estimated scores and the ground truths, suffering from the issue of data inconsistency. The data inconsistency caused by various measurements, e.g., Kd, Ki, and IC50, as well as experimental conditions, e.g., reactant concentration and temperature, severely hinders the effective utilization of existing data, thus deteriorating the performance of DTA prediction. We propose a novel paradigm for effective training on hybrid DTA data to alleviate the data inconsistency issue. Since the ranking orders of the affinity scores with respect to measurements and experimental batches are more consistent, we adopt a pairwise paradigm to enable the DNNs to learn from ranking orders instead. We expect this paradigm can effectively blend datasets with various measurements and experimental batches to achieve better performances. For the sake of verifying the proposed paradigm, we compare it with the previous paradigm for various model backbones on multiple DTA datasets. The experimental results demonstrate the superior performance of our proposed paradigm. The ablation studies also show the effectiveness of the design of the proposed training paradigm.

Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting

The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when the model is encouraged to remember the evidence for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for the Right Reasons (RRR), that additionally stores visual model explanations for each example in the buffer and ensures the model has “the right reasons” for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization-based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in the standard and few-shot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at \url{https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons}

Family of Origin as Cultural Messengers in the Training Context

A novel Family of Origin as Supervisory Resource Model that harnesses the family of origin of the therapist-in-training as a cultural supervisory resource in the training paradigm is presented. The format of the model comprises three phases: supervisor’s exploration of the trainee’s professional dilemmas, supervisor’s exploration of the same dilemma through stories narrated by the supervisee’s family of origin members, and supervisee’s presentation of a case that is an example of the dilemma. The application of this training model during supervision strengthens the trainee’s flexibility in divergent family systems and enhances the ability to handle dilemmas and to form a healthy therapeutic alliance.

Soft Skills & Metacognition as Inclusion Amplifiers in the 21st Century

In the present paper we investigate soft skills in the light of metacognition. We seek the essential soft skills in the 21^st century including green skills and look into their cognitive and metacognitive background. Enlightening the soft skills’ dependence on metacognition, we conclude on a metacognition-based approach and suggest useful tools and strategies. The metacognitive approach of soft skills can be applied in a variety of educational contexts as a training paradigm to accelerate the inclusion and success of students, employees and citizens, especially those belonging in vulnerable groups like persons with disabilities.

The Implementation of Velocity-Based Training Paradigm for Team Sports: Framework, Technologies, Practical Recommendations and Challenges

While velocity-based training is currently a very popular paradigm to designing and monitoring resistance training programs, its implementation remains a challenge in team sports, where there are still some confusion and misinterpretations of its applications. In addition, in contexts with large squads, it is paramount to understand how to best use movement velocity in different exercises in a useful and time-efficient way. This manuscript aims to provide clarifications on the velocity-based training paradigm, movement velocity tracking technologies, assessment procedures and practical recommendations for its application during resistance training sessions, with the purpose of increasing performance, managing fatigue and preventing injuries. Guidelines to combine velocity metrics with subjective scales to prescribe training loads are presented, as well as methods to estimate 1-Repetition Maximum (1RM) on a daily basis using individual load–velocity profiles. Additionally, monitoring strategies to detect and evaluate changes in performance over time are discussed. Finally, limitations regarding the use of velocity of execution tracking devices and metrics such as “muscle power” are commented upon.