Evaluation of Medical Students’ Attitudes and Performance of Basic Surgery Skills in a Training Program Using Fresh Human skin, Excised During Body Contouring Surgeries

BACKGROUND: Learning surgical skills in the operating room may be a challenge for medical students. Therefore, more approaches using simulation to enable students to develop their practical skills are required. OBJECTIVES: We hypothesized that (1) there would be a need for additional surgical training for medical students in the pre-final year, and (2) our basic surgery skills training program using fresh human skin would improve medical students' surgical skills. DESIGN: We conducted a preliminary survey of medical students to clarify the need for further training in basic surgery procedures. A new approach using simulation to teach surgical skills on human skin was set up. The procedural skills of 15 randomly selected students were assessed in the operating room before and after participation in the simulation, using Objective Structured Assessment of Technical Skills. Furthermore, subjective assessment was performed based on students' self-evaluation. The data were analyzed using SPSS, version 21 (SPSS, Inc., Chicago, IL). SETTING: The study took place at the Inselspital, Bern University Hospital. PARTICIPANTS: A total of 186 pre-final-year medical students were enrolled into the preliminary survey; 15 randomly selected medical students participated in the basic surgical skills training course on the fresh human skin operating room. RESULTS: The preliminary survey revealed the need for a surgical skills curriculum. The simulation approach we developed showed significant (p < 0.001) improvement for all 12 surgical skills, with mean cumulative precourse and postcourse values of 31.25 ± 5.013 and 45.38 ± 3.557, respectively. The self-evaluation contained positive feedback as well. CONCLUSION: Simulation of surgery using human tissue samples could help medical students become more proficient in handling surgical instruments before stepping into a real surgical situation. We suggest further studies evaluating our proposed teaching method and the possibility of integrating this simulation approach into the medical school curriculum.

Evaluation of a web-based application versus conventional instruction in the undergraduate curriculum of fixed prosthodontics

The present study aims to investigate the implications of web-based delivery of identical learning content for time efficiency and students' performance, as compared to conventional textbook resources.

First clinical trial of tomographic neurofeedback in attention-deficit/hyperactivity disorder: evaluation of voluntary cortical control

Tomographic neurofeedback (tNF) training was evaluated as a treatment for attention-deficit/hyperactivity disorder (ADHD). To investigate the specificity of the treatment, outcomes were related to learning during tNF.

Improving executive functions in 5- and 6-year-olds: Evaluation of a small group intervention in prekindergarten and kindergarten children

Research suggests a central role of executive functions for children's cognitive and social development during preschool years, especially in promoting school readiness. Interventions aiming to improve executive functions are therefore being called for. The present study examined the effect of a small group intervention implemented in kindergarten settings focusing on basic components of executive functions, i.e., working memory, interference control and cognitive flexibility. A total of 135 children enrolled in Swiss prekindergarten (5-year-olds) and kindergarten (6-year-olds) were involved. Results revealed that the small group intervention promoted gains in all three included components of executive functions: prekindergarten children substantially improved their working memory and cognitive flexibility processes, whereas significant training effects were found for the kindergarten children in interference control. Implications of these findings for early intervention programs and for tailoring preschool curricula are discussed, particularly with respect to children's school readiness. Copyright © 2011 John Wiley & Sons, Ltd.

An epidemiological evaluation of pediatric long bone fractures - a retrospective cohort study of 2716 patients from two Swiss tertiary pediatric hospitals.

BACKGROUND Children and adolescents are at high risk of sustaining fractures during growth. Therefore, epidemiological assessment is crucial for fracture prevention. The AO Comprehensive Injury Automatic Classifier (AO COIAC) was used to evaluate epidemiological data of pediatric long bone fractures in a large cohort. METHODS Data from children and adolescents with long bone fractures sustained between 2009 and 2011, treated at either of two tertiary pediatric surgery hospitals in Switzerland, were retrospectively collected. Fractures were classified according to the AO Pediatric Comprehensive Classification of Long Bone Fractures (PCCF). RESULTS For a total of 2716 patients (60% boys), 2807 accidents with 2840 long bone fractures (59% radius/ulna; 21% humerus; 15% tibia/fibula; 5% femur) were documented. Children's mean age (SD) was 8.2 (4.0) years (6% infants; 26% preschool children; 40% school children; 28% adolescents). Adolescent boys sustained more fractures than girls (p < 0.001). The leading cause of fractures was falls (27%), followed by accidents occurring during leisure activities (25%), at home (14%), on playgrounds (11%), and traffic (11%) and school accidents (8%). There was boy predominance for all accident types except for playground and at home accidents. The distribution of accident types differed according to age classes (p < 0.001). Twenty-six percent of patients were classed as overweight or obese - higher than data published by the WHO for the corresponding ages - with a higher proportion of overweight and obese boys than in the Swiss population (p < 0.0001). CONCLUSION Overall, differences in the fracture distribution were sex and age related. Overweight and obese patients seemed to be at increased risk of sustaining fractures. Our data give valuable input into future development of prevention strategies. The AO PCCF proved to be useful in epidemiological reporting and analysis of pediatric long bone fractures.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.