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ellular model of steatosis. Histone 3 lysine 9 methylation should be considered together with histone 3 lysine 4 and histone 3 lysine 27 methylation as the epigenetic mechanisms controlling gene expression of P450s.There is a well-known knowledge gap regarding the efficacy and safety of medicines in children of all ages and children are often treated with medicines off-label. Children are thus deprived of treatment based on the same quality of information that guides treatment in adults. The knowledge gap regarding efficacy and safety of medicines in children has been acknowledged by authorities and is reflected in legislation both in North America and in the European Union. Recent reports on the effects of legislation indicates that paediatric clinical trials remain a challenge.Paediatric clinical trials are needed in the entire developmental age spectrum and are especially needed in certain therapy areas. Paediatric clinical trials have special features compared with trials in adults, and these need to be taken into account. check details These special features include scientific issues related to small samples and heterogeneity, the consent/assent procedure, the need for age-appropriate study information, specific outcomes and safety issues related to development and maturation. Competence in paediatric clinical trials is required in both designing, planning, co-ordinating and organising paediatric clinical trials, as well as research infrastructure and networks to increase power and disseminate information and expert advice. Strengthening of paediatric clinical research is essential to facilitate generating the data that will let children enjoy new medical advances in a similar manner as adults.

Teaching and assessment of complex problem solving are a challenge for medical education. Integrating Machine Learning (ML) into medical education has the potential to revolutionize teaching and assessment of these problem-solving processes. In order to demonstrate possible applications of ML to education, we sought to apply ML in the context of a structured Video Commentary (VC) assessment, using ML to predict residents’ training level.

A secondary analysis of multi-institutional, IRB approved study. Participants had completed the VC assessment consisting of 13 short (20-40 seconds) operative video clips. They were scored in real-time using an extensive checklist by an experienced proctor in the assessment. A ML model was developed using TensorFlow and Keras. The individual scores of the 13 video clips from the VC assessment were used as the inputs for the ML model as well as for regression analysis.

A total of 81 surgical residents of all postgraduate years (PGY) 1-5 from 7 institutions constituted tharily foretelling of a higher PGY level. The use of the total score as a sole measure may fail to detect deeper relationships. Our ML model is a promising tool in gauging learners’ levels on an assessment as extensive as VC. The model managed to approximate residents’ PGY levels with a lower MAE than using traditional statistics. Further investigations with larger datasets are needed.

Laparoscopic simulation is widely used in surgical training. However, the impact of training on performance is difficult to assess. Observation is time-intensive and subjective. SurgTrac laparoscopic box-trainer instrument tracking software provides continuous, automated, real-time, objective performance feedback. We used this data to assess the relationship between task attempts and performance. We assessed whether improvement in performance with repetition could be modeled in learning curves that might be used for benchmarking.

Anonymized SurgTrac data for performances undertaken between 10/2016 and 05/2019 were retrospectively extracted. The thread transfer task, a basic instrument handling task, was assessed. Task duration and instrument-based metrics were analyzed; total distance travelled by instrument tips, average speed, average acceleration, and the ratio of movements between the left and right hands. Curve estimation regression was used to assess the relationship between attempt number and metries derived from peer-group performances as benchmarks, users may be regularly and objectively assessed to support personalization of training.

We analyzed the largest database of simulated laparoscopic task performances. Performance improves with practice. Using learning curves derived from peer-group performances as benchmarks, users may be regularly and objectively assessed to support personalization of training.

Institutions training both General Surgery (GS) residents and Hepato-Pancreatico-Biliary (HPB) fellows must strive for adequate case volumes for each trainee cohort.

Six academic years of graduating ACGME Residency and HPB Fellowship Council case logs (July 2011-June 2017) and institutional administrative faculty billing data were examined at a single high-volume center with a formal HPB Surgical Division with both GS Residency and HPB Surgery Fellowship trainees.

During the 6-year period, 7482 operations were performed by HPB faculty (5.5 total full-time equivalent (FTE)) and included 2419 major liver, 375 major biliary, and 1591 major pancreas cases. Residents/fellows performed 1102 (50%)/1101 (50%) of all major liver operations, 165 (49.7%)/163 (50.3%) major biliary operations, and 843 (59.2%)/581 (40.8%) major pancreas operations, with significantly different case mix of pancreas for resident versus fellow, p < 0.0001. The overall relative proportion of total HPB cases performed by residents versus fellows was 53%/47%, respectively, and this was stable over time, with no significant decrease in resident exposure/cases with dedicated HPB fellowship.

Our experience in training both GS residents and HPB fellows with a formal HPB Surgical Division suggests that a high volume HPB Division allows for more than adequate exposure for both groups of trainees.

Our experience in training both GS residents and HPB fellows with a formal HPB Surgical Division suggests that a high volume HPB Division allows for more than adequate exposure for both groups of trainees.