A study to explore if dentists’ anxiety affects their clinical decision-making

Aims To develop a measure of dentists’ anxiety in clinical situations; to establish if dentists’ anxiety in clinical situations affected their self-reported clinical decision-making; to establish if occupational stress, as demonstrated by burnout, is associated with anxiety in clinical situations and clinical decision-making; and to explore the relationship between decision-making style and the clinical decisions which are influenced by anxiety. Design Cross-sectional study. Setting Primary Dental Care. Subjects and methods A questionnaire battery [Maslach Burnout Inventory, measuring burnout; Melbourne Decision Making Questionnaire, measuring decision-making style; Dealing with Uncertainty Questionnaire (DUQ), measuring coping with diagnostic uncertainty; and a newly designed Dentists’ Anxieties in Clinical Situations Scale, measuring dentists’ anxiety (DACSS-R) and change of treatment (DACSS-C)] was distributed to dentists practicing in Nottinghamshire and Lincolnshire. Demographic data were collected and dentists gave examples of anxiety-provoking situations and their responses to them. Main outcome measure Respondents’ self-reported anxiety in various clinical situations on a 11-point Likert Scale (DACSS-R) and self-reported changes in clinical procedures (Yes/No; DACSS-C). The DACSS was validated using multiple t-tests and a principal component analysis. Differences in DACSS-R ratings and burnout, decision-making and dealing with uncertainty were explored using Pearson correlations and multiple regression analysis. Qualitative data was subject to a thematic analysis. Results The DACSS-R revealed a four-factor structure and had high internal reliability (Cronbach’s α = 0.94). Those with higher DACSS-R scores of anxiety were more likely to report changes in clinical procedures (DACSS-C scores). DACSS-R scores were associated with decision-making self-esteem and style as measured by the MDMQ and all burnout subscales, though not with scores on the DUQ scale. Conclusion Dentists’ anxiety in clinical situations does affect the way that dentists work clinically, as assessed using the newly designed and validated DACSS. This anxiety is associated with measures of burnout and decision-making style with implications for training packages for dentists

Enhanced CellClassifier: a multi-class classification tool for microscopy images

BACKGROUND: Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories. RESULTS: We have developed a tool, Enhanced CellClassifier, which circumvents this obstacle. Enhanced CellClassifier starts from images analyzed by CellProfiler, and allows multi-class classification using a Support Vector Machine algorithm. Training of objects can be done by clicking directly "on the microscopy image" in several intuitive training modes. Many routine tasks like out-of focus exclusion and well summary are also supported. Classification results can be integrated with other object measurements including inter-object relationships. This makes a detailed interpretation of the image possible, allowing the differentiation of many complex phenotypes. For the generation of the output, image, well and plate data are dynamically extracted and summarized. The output can be generated as graphs, Excel-files, images with projections of the final analysis and exported as variables. CONCLUSION: Here we describe Enhanced CellClassifier which allows multiple class classification, elucidating complex phenotypes. Our tool is designed for the biologist who wants both, simple and flexible analysis of images without requiring programming skills. This should facilitate the implementation of automated high-content screening

Environmental risk assessments for transgenic crops producing output trait enzymes

The environmental risks from cultivating crops producing output trait enzymes can be rigorously assessed by testing conservative risk hypotheses of no harm to endpoints such as the abundance of wildlife, crop yield and the rate of degradation of crop residues in soil. These hypotheses can be tested with data from many sources, including evaluations of the agronomic performance and nutritional quality of the crop made during product development, and information from the scientific literature on the mode-of-action, taxonomic distribution and environmental fate of the enzyme. Few, if any, specific ecotoxicology or environmental fate studies are needed. The effective use of existing data means that regulatory decision-making, to which an environmental risk assessment provides essential information, is not unnecessarily complicated by evaluation of large amounts of new data that provide negligible improvement in the characterization of risk, and that may delay environmental benefits offered by transgenic crops containing output trait enzymes

The evaluation of a continuing professional development package for primary care dentists designed to reduce stress, build resilience and improve clinical decision-making

Introduction: Stress and burnout are widely accepted as a problem for primary care dental practitioners. Previous programmes to address this issue have met with some success. Burnout is associated with poor coping skills and emotion regulation, and increased rates of clinical errors. Anxiety is associated with poor decision-making and is thought to be associated with poor clinical decision-making. Attempts to improve decision-making use increasing meta-awareness and review of thinking processes. Bibliotherapy is an effective method of delivering cognitive behavioural therapy as self-help or guided self-help (with some therapist input) formats. Objective: To evaluate the efficacy of a specially designed CPD package which was designed to improve coping skills, build resilience and reduce the impact of anxiety on dentists’ clinical decision-making. Design: A multi-centred quasi-experiment Setting: Lincolnshire and Nottinghamshire (England) 2014 Materials and methods: Thirty-five volunteer primary care dentists used two versions (self-help [SH] and guided self-help [GSH], which included a 3 hour workshop) of a specially written cognitive-behavioural-therapy bibliotherapy programme designed to improve well-being and decision-making. Main Outcome Measures: The main outcome measures were dentists’ burnout, depression, anxiety, stress and decision-making style. Data were also collected on use and evaluation of the programme. Results: At 6 weeks there was a clinically and statistically significant reduction in depression, anxiety and stress levels, a statistically significant reduction in burnout (emotional exhaustion) and hypervigilant decision-making and an increase in personal achievement (burnout). The improvements in depression, stress, emotional exhaustion and hypervigilant decision-making were maintained at 6 months. Dentists were overwhelmingly positive in their evaluation of the project and used most of its contents. Conclusion: With the caveat of small numbers and the lack of a no-treatment control, this project demonstrated that a self-help package can be highly acceptable to dentists and, in the short-to-medium term, improve dentists’ well-being and decision-making with implications for patient safety

Combining deep learning and structured prediction for segmenting masses in mammograms

The segmentation of masses from mammogram is a challenging problem because of their variability in terms of shape, appearance and size, and the low signal-to-noise ratio of their appearance. We address this problem with structured output prediction models that use potential functions based on deep convolution neural network (CNN) and deep belief network (DBN). The two types of structured output prediction models that we study in this work are the conditional random field (CRF) and structured support vector machines (SSVM). The label inference for CRF is based on tree re-weighted belief propagation (TRW) and training is achieved with the truncated fitting algorithm; whilst for the SSVM model, inference is based upon graph cuts and training depends on a max-margin optimization. We compare the results produced by our proposed models using the publicly available mammogram datasets DDSM-BCRP and INbreast, where the main conclusion is that both models produce results of similar accuracy, but the CRF model shows faster training and inference. Finally, when compared to the current state of the art in both datasets, the proposed CRF and SSVM models show superior segmentation accuracy. © Springer International Publishing Switzerland 2017