Breast Implant-associated Anaplastic Large Cell Lymphoma: A Canadian Surgical Oncology Survey.

BACKGROUND: Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) awareness has increased, resulting in concerns regarding the safety of implant-based reconstruction. Breast cancer patients are first seen by surgical oncologists, who are therefore potentially the first health-care professionals to encounter concerns regarding BIA-ALCL. We therefore surveyed surgical oncologists on their understanding of BIA-ALCL to better assess potential effects on plastic surgery practice. METHODS: An anonymous web-based survey consisting of 9 multiple-choice questions was sent to breast surgical oncologists that are members of the Canadian Society of Surgical Oncology (n = 135). RESULTS: Forty-two members responded (n = 42/135, 31%) and all participants were aware of BIA-ALCL. All participants reported that BIA-ALCL has not deterred them from referring patients for implant-based reconstruction. Twenty-two respondents (52%) discuss BIA-ALCL with their patients and 21% (n = 9) believe that BIA-ALCL typically follows a metastatic course. Eight respondents (19%) reported having a poor understanding of BIA-ALCL, while 14% (n = 6) were unable to identify the link to textured implants. There were no statistical differences based on case-load volume. CONCLUSIONS: Approximately half of the respondent Canadian breast surgical oncologists discuss BIA-ALCL with their patients, yet there is a knowledge gap in terms of the epidemiology and clinical-pathological course of BIA-ALCL. It is of utmost importance to ensure that the plastic surgery community aims at including surgical oncologist colleagues in educational platforms regarding BIA-ALCL to ensure collaboration and unity in an effort to offer the most accurate information to patients, and prevent misinformation that may deter patients from seeking implant-based reconstruction.

Machine learning distinguishes neurosurgical skill levels in a virtual reality tumor resection task.

This study outlines the first investigation of application of machine learning to distinguish "skilled" and "novice" psychomotor performance during a virtual reality (VR) brain tumor resection task. Tumor resection task participants included 23 neurosurgeons and senior neurosurgery residents as the "skilled" group and 92 junior neurosurgery residents and medical students as the "novice" group. The task involved removing a series of virtual brain tumors without causing injury to surrounding tissue. Originally, 150 features were extracted followed by statistical and forward feature selection. The selected features were provided to 4 classifiers, namely, K-Nearest Neighbors, Parzen Window, Support Vector Machine, and Fuzzy K-Nearest Neighbors. Sets of 5 to 30 selected features were provided to the classifiers. A working point of 15 premium features resulted in accuracy values as high as 90% using the Supprt Vector Machine. The obtained results highlight the potentials of machine learning, applied to VR simulation data, to help realign the traditional apprenticeship educational paradigm to a more objective model, based on proven performance standards. Graphical abstract Using several scenarios of virtual reality neurosurgical tumor resection together with machine learning classifiers to distinguish skill level.

Step-Specific Simulation: The Utility of 3D Printing for the Fabrication of a Low-Cost, Learning Needs-Based Rhinoplasty Simulator.

BACKGROUND: Rhinoplasty is a complex procedure that requires meticulous planning and precise execution. Plastic surgeons involved in teaching residents must balance a trainee's hands-on experience while ensuring appropriate execution of difficult maneuvers. Surgical simulation, a field of growing importance with the shift towards competency-based education, may aid in trainee skill development. Through the concept of deliberate practice, the authors looked to explore the utility and economics of 3-dimensional (3D) printing technology to develop a step-specific rhinoplasty simulator. OBJECTIVES: The main objective of this study was to address rhinoplasty skills previously identified as "learning areas of weakness" and develop a low-cost, step-specific simulator to help rhinoplasty teaching. METHODS: A patient's facial bones, upper and lower lateral cartilages, and septum were segmented from a computed tomography scan and rendered in 3D format. This was 3D printed utilizing Ultimaker Polylactic filament with a polyvinyl acetate dissolvable support for bone, a mixture of Rigur 450 and Tango plus polyjet material for cartilage, and Smooth-On Dragon Skin for skin. RESULTS: A modular simulator was developed with 3 separate, interchangeable components with a perfect fit design. The simulator allowed for deliberate practice of the 5 rhinoplasty learning areas of weakness, with a maximal recurring cost of $75 CAD. CONCLUSIONS: Through the employment of 3D printing, a low-cost, maneuver-specific rhinoplasty simulator reinforcing deliberate practice was developed. This concept of simulation-based deliberate practice may be of increasing interest when considering the implementation of competency-based curricular standards in plastic surgery education.

Reevaluating the Current Model of Rhinoplasty Training and Future Directions: A Role for Focused, Maneuver-Specific Simulation.

BACKGROUND: Rhinoplasty is known for its complexity in planning and execution. For trainees, knowledge acquisition is often adequately attained. The mastery of skills, however, occurs by means of hands-on exposure, which continues to be a challenge. This article discusses the positive progress made in rhinoplasty training, and objectively demonstrates a need for more hands-on rhinoplasty exposure for residents. METHODS: A systematic review was performed in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Concurrently, an online survey was developed, assessing resident comfort and training in rhinoplasty, and e-mailed to Canadian and U.S. plastic surgery training programs. RESULTS: ONE HUNDRED THIRTY-EIGHT: residents completed the survey, 62 junior (first- to third-year residents) and 76 senior residents (fourth- to sixth-year residents). Seventy-two percent of senior residents (95 percent of sixth-year residents) reported adequate rhinoplasty exposure, as opposed to 13 percent of junior residents. Seventy-five percent of senior residents most often participated as observers or first assistants, 25 percent participated as co-surgeons, and 73.9 percent did not perform a key rhinoplasty step more than five times. Residents felt the three most difficult steps of rhinoplasty were nasal osteotomy (76.1 percent), caudal septum/anterior nasal spine manipulation (65.2 percent), and nasal tip sutures (55.8 percent), and 73.9 percent felt that simulator training would substantially improve confidence. CONCLUSIONS: Despite sufficient exposure to rhinoplasties, residents were least confident in performing rhinoplasties relative to other aesthetic procedures, likely because of the high proportion of rhinoplasty exposure that is observational as opposed to hands-on acquisition of surgical maneuvers in the operating room. The survey established the maneuvers residents find the most difficult, and as programs adopt competency-based training, developing rhinoplasty simulators targeting specific identified steps may help improve competence for rhinoplasty skills.

Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation.

OBJECTIVE: Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analyzed with machine learning algorithms. These advancements may increase the understanding, assessment and training of psychomotor performance. Consequently, the application of machine learning techniques to evaluate performance on virtual reality simulators has led to an increase in the volume and complexity of publications which bridge the fields of computer science, medicine, and education. Although all disciplines stand to gain from research in this field, important differences in reporting exist, limiting interdisciplinary communication and knowledge transfer. Thus, our objective was to develop a checklist to provide a general framework when reporting or analyzing studies involving virtual reality surgical simulation and machine learning algorithms. By including a total score as well as clear subsections of the checklist, authors and reviewers can both easily assess the overall quality and specific deficiencies of a manuscript. DESIGN: The Machine Learning to Assess Surgical Expertise (MLASE) checklist was developed to help computer science, medicine, and education researchers ensure quality when producing and reviewing virtual reality manuscripts involving machine learning to assess surgical expertise. SETTING: This study was carried out at the McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre. PARTICIPANTS: The authors applied the checklist to 12 articles using machine learning to assess surgical expertise in virtual reality simulation, obtained through a systematic literature review. RESULTS: Important differences in reporting were found between medical and computer science journals. The medical journals proved stronger in discussion quality and weaker in areas related to study design. The opposite trends were observed in computer science journals. CONCLUSIONS: This checklist will aid in narrowing the knowledge divide between computer science, medicine, and education: helping facilitate the burgeoning field of machine learning assisted surgical education.

Initiative for Promoting Affordable and Quality Tuberculosis Tests (IPAQT): a market-shaping intervention in India.

A majority of patients with tuberculosis (TB) in India are diagnosed and treated in the private sector. Yet, most private providers do not use most recent WHO-endorsed microbiological tests such as liquid cultures, line probe assays and Xpert MTB/RIF due to a combination of factors such as lack of awareness, misaligned incentives and high prices that are unaffordable for patients. We designed a market-based approach to transform a high-price, low-volume market equilibrium into a low-price, high-volume equilibrium to improve the uptake of these tests. Toward this end, a non-profit consortium of private laboratories, called Initiative for Promoting Affordable and Quality Tuberculosis Tests (IPAQT) was formed in India in March 2013. It negotiated lower pricing on equipment and reagents with manufacturers, closer to that offered to the public sector. In return, IPAQT assured that this discount was passed on to patients, who typically paid for these tests out of their pockets, through an informally agreed on retail ceiling price. IPAQT also invested in demand generation activities that complemented the supply-side effort. IPAQT membership grew from 56 laboratories in 2013 to 211 in 2018. During this period, the initiative resulted in a 10-fold increase in the uptake of Xpert and a 30%-50% reduction in price. This initiative is planned to be expanded to other South Asian countries with similar TB epidemic and private market structure and dynamics. However, long-term sustainability of the initiative would require developing more cost-effective marketing activities and integration with broader private sector engagement agenda of the national TB programme.