Search

The PLOS Medicine Editors discuss issues of vaccination uptake in the context of recent and ongoing measles outbreaks. [ABSTRACT FROM AUTHOR]

Purpose: Therapeutic drug monitoring (TDM) is widely recognised as a key attribute of clinical pharmacologists; yet, the extent to which physicians undertaking postgraduate training in clinical pharmacology (hereafter trainees) are involved in TDM is poorly characterised. Our own experience suggests wide variation in trainee exposure to TDM. Method: We performed a Europe-wide cross-sectional internet-based survey of trainees to determine the nature and extent of trainee involvement in TDM. Results: There were 43 responses from eight countries analysed. Of the 21 respondents from the UK, all were also training in general internal medicine (GIM), while all of the respondents who were solely training in clinical pharmacology were from outside the UK. Overall, 86.0% of respondents reported access to drug monitoring for clinical care at their affiliated institution, of which 81.0% were personally involved in TDM in some capacity. On average, trainees reported that drug monitoring was available for 16 of the 33 (48%) of the drug/drug classes surveyed. UK-based respondents were involved in requesting drug-level investigations and interpreting the results for patients under their care in 76.2% and 85.7% of cases, respectively, while non-UK respondents supported other healthcare professionals to interpret results in 45.4% of cases. Trainees felt TDM training was generally either insufficient or very inadequate. Conclusion: While access to TDM is relatively available at institutions where trainees are based, the role of trainees is variable and affected by a variety of factors including country and training programme. Universally, trainees feel they need more education in TDM. [ABSTRACT FROM AUTHOR]

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies. [ABSTRACT FROM AUTHOR]

Background and Objective: The present survey was a preliminary to a European research project on the attitude and knowledge of healthcare professionals towards the use of medical cannabis. The objective was to evaluate the readability, understandability, and relevance of a first version of the study questionnaire before preparing the finalized questionnaire, which will be subsequently proposed to European healthcare professionals on a large scale. Methods: A cross-sectional study was conducted between December 2019 and May 2020. We established an electronic evaluation questionnaire relating to the study questionnaire. This evaluation questionnaire was proposed to multidisciplinary experts from all over Europe. Feedback from the evaluation questionnaire was considered for improving and finalizing the study questionnaire. Results: 66 evaluation questionnaires were collected from nine European countries (Cyprus, France, Germany, Italy, Lithuania, Portugal, Spain, Sweden, United Kingdom), which corresponded to a participation rate of 41.5%. Most participants were women (65.2%, n = 43). The mean age was 39.5 years ± 11.6. Each participant could specify several occupations. There were 25 pharmacologists, 24 physicians, ten pharmacists, four university teachers, three epidemiologists or public health experts, one nurse, one biotechnologist, one microbiologist, and one police researcher. Overall, 84.8% of participants were interested in the topic of the survey on the knowledge and attitudes of healthcare professionals towards recreational and medical cannabis across Europe. Participants were satisfied with all but six of the proposed questions. In addition, two additional questions were subject for comments despite a high level of satisfaction. Consequently, the concerned questions (n = 8) were modified. Conclusion: This evaluation survey was a necessary step to improve the quality of the future research project. The positive feedback encourages the authors to proceed with the project on a European scale, scheduled for 2021. [ABSTRACT FROM AUTHOR]

A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep learning techniques to be applied to off-the-grid spatio-temporal data. This model has a substantial advantage over existing downscaling methods in that the trained model can be used to generate multisite predictions at an arbitrary set of locations, regardless of the availability of training data. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies, and motivates further research into applications of deep learning techniques in statistical downscaling. [ABSTRACT FROM AUTHOR]