Implementing failure mode and effect analysis to improve the safety of volumetric modulated arc therapy for total body irradiation.

PURPOSE: Volumetric-modulated arc therapy for total body irradiation (VMAT-TBI) is a novel radiotherapy technique that has been implemented at our institution. The purpose of this work is to investigate possible failure modes (FMs) in the treatment process and to develop a quality control (QC) program for VMAT-TBI following TG-100 guidelines. METHODS: We formed a multidisciplinary team to map out the complete treatment process of VMAT-TBI following the AAPM TG-100 guidelines. This process map gives a visual representation of the VMAT-TBI workflow from the CT simulation, image processing, contouring, treatment planning, to treatment delivery. From the process map, potential FMs were identified. The occurrence (O), detectability (D), and severity of impact (S) of each FM were assigned according to scoring criteria (1-10) by the multidisciplinary team. A risk priority number (RPN) was calculated from average O, S, and D of each FM (RPN = O x S x D). High risk FMs were identified as 20% of the FMs having the highest RPN scores. After the FMEA analysis, fault-tree analysis (FTA) was performed for each major step of the treatment process to determine the effects of potential failures to the treatment outcome. Effective QC methods were identified to prevent the high risk failures and to improve the safety of the VMAT-TBI program. RESULTS: We identified a total of 55 sub-processes and 128 FMs from the VMAT-TBI workflow. The top five high-risk FMs were: (1) Prescription and/or OAR constraints changed during planning and not communicated to the planner, (2) Patient moves or breathes too heavily during the upper body CT scan (3) Patient moves during the lower body CT scan, (4) Treatment planning system not calculating total body DVH metrics correctly for TBI, (5) Improper optimization criteria used or not sufficient optimization, resulting in suboptimal dose coverage, OAR sparing or excessive hotspots during treatment planning. Two FMs have average severity scores ≥8: Incorrect PTV subdivision/isocenter placement and Prescription and/or OAR constraints changed during planning and not communicated to the planner. Quality assurance and QC interventions including staff training, standard operating procedures, and quality checklists were implemented based on the FMEA and FTA. CONCLUSION: FM and effect analysis was performed to identify high-risk FMs of our VMAT-TBI program. FMEA and FTA were effective in identifying potential FMs and determining the best quality management (QM) measures to implement in the VMAT-TBI program.

Developing Students' Emotional Competencies in English Language Classes: Reciprocal Benefits and Practical Implications.

Learning a foreign language involves a wide range of cognitive, social and affective skills. The present article gives ideas to develop socio-emotional competencies in English courses: the capacity to identify the emotion, to understand the causes and consequences, to express their emotions and to do so in a socially acceptable manner, to manage stress and to use their emotions to increase the effectiveness of thinking, decision making and actions. Content and language integrated learning (CLIL) is a dual approach aiming to develop both language and academic subject knowledge. It may be gradually introduced, embedding it at three levels: into the classroom (routines, organization, pupils' behavior), the school and the curriculum. Successful learning in CLIL remains based on (1) communication, (2) ways of engaging in the learning process and (3) the use of meaning-making strategies. We propose a pedagogical sequence (several courses) to learn a second language based on the social and emotional learning approach, and the English coursebook MORE! 7e for primary school pupils (aged 10-11). We combine the specific language learning of the unit-talking about ourselves, people and their feelings-with the development of pupils' basic emotional competencies, and discuss advantages and disadvantages to consider in order to successfully implement such lessons.

New Jersey COVID-19 municipal dataset.

Although data about COVID-19 cases and deaths in the United States are readily available at the county-level, datasets on smaller geographic areas are limited. County-level data have been used to identify geospatial patterns of COVID-19 spread and, in conjunction with sociodemographic variables, have helped identify population health disparities concerning COVID-19 in the US. Municipality-level data are essential for advancing more targeted and nuanced understanding of geographic-based risk and resilience associated with COVID-19. We created a dataset that tracks COVID-19 cases and deaths by municipalities in the state of New Jersey (NJ), US, from April 22, 2020 to December 31, 2020. Data were drawn primarily from official county and municipality websites. The dataset is a spreadsheet containing cumulative case counts and case rates in each municipaly on three target dates, representing the peak of the first wave, the summer trough after the first wave, and the outbreak of the second wave in NJ. This dataset is valuable for four main reasons. First, the dataset is unique, because New Jersey's Health Department does not release COVID-19 data for the 77% (433/565) of municipalities with populations smaller than 20,000 individuals. Second, especially when combined with other data sources, such as publicly available sociodemographic data, this dataset can be used to advance epidemiological research on geographic differences in COVID-19, as well as to inform decision-making concerning the allocation of resources in response to the pandemic (e.g., strategies for targeted vaccine outreach campaigns). Third, county-level data mask important variations across municipalities, so municipality-level data permit a more nuanced exploration of health disparities related to local demographics, socioeconomic conditions, and access to resources and services. New Jersey is a good state to explore these patterns, because it is the most densely-populated and racially/ethnically diverse state in the US. Fourth, New Jersey was one of the few locations in the US with a high prevalence of COVID-19 during the first wave of the pandemic in the US. Thus, this dataset permits exploration of whether sociodemographic variables predicted COVID-19 differently as time progressed. To summarize, this unique municipality-level dataset in a diverse state with high COVID-19 cases is valuable for scholars and policy analysts to explore social and environmental factors related to the prevalence and transmission of COVID-19 in the US.