Associations of workflow disruptions in the operating room with surgical outcomes: a systematic review and narrative synthesis

Background: Performance in the operating room is an important determinant of surgical safety. Flow disruptions (FDs) represent system-related performance problems that affect the efficiency of the surgical team and have been associated with a risk to patient safety. Despite the growing evidence base on FDs, a systematic synthesis has not yet been published. Objective: Our aim was to identify, evaluate and summarise the evidence on relationships between intraoperative FD events and provider, surgical process and patient outcomes. Methods We systematically searched databases MEDLINE, Embase and PsycINFO (last update: September 2019). Two reviewers independently screened the resulting studies at the title/abstract and full text stage in duplicate, and all inconsistencies were resolved through discussion. We assessed the risk of bias of included studies using established and validated tools. We summarised effects from included studies through a narrative synthesis, stratified based on predefined surgical outcome categories, including surgical process, provider and patient outcomes. Results We screened a total of 20 481 studies. 38 studies were found to be eligible. Included studies were highly heterogeneous in terms of methodology, medical specialty and context. Across studies, 20.5% of operating time was attributed to FDs. Various other process, patient and provider outcomes were reported. Most studies reported negative or non-significant associations of FDs with surgical outcomes. Conclusion Apart from the identified relationship of FDs with procedure duration, the evidence base concerning the impact of FDs on provider, surgical process and patient outcomes is limited and heterogeneous. We further provide recommendations concerning use of methods, relevant outcomes and avenues for future research on associated effects of FDs in surgery

MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data

Few-shot relation classification seeks to classify incoming query instances after meeting only few support instances. This ability is gained by training with large amount of in-domain annotated data. In this paper, we tackle an even harder problem by further limiting the amount of data available at training time. We propose a few-shot learning framework for relation classification, which is particularly powerful when the training data is very small. In this framework, models not only strive to classify query instances, but also seek underlying knowledge about the support instances to obtain better instance representations. The framework also includes a method for aggregating cross-domain knowledge into models by open-source task enrichment. Additionally, we construct a brand new dataset: the TinyRel-CM dataset, a few-shot relation classification dataset in health domain with purposely small training data and challenging relation classes. Experimental results demonstrate that our framework brings performance gains for most underlying classification models, outperforms the state-of-the-art results given small training data, and achieves competitive results with sufficiently large training data

How Will Hydroelectric Power Generation Develop under Climate Change Scenarios?

Climate change has a large impact on water resources and thus on hydropower. Hydroelectric power generation is closely linked to the regional hydrological situation of a watershed and reacts sensitively to changes in water quantity and seasonality. The development of hydroelectric power generation in the Upper Danube basin was modelled for two future decades, namely 2021-2030 and 2051-2060, using a special hydropower module coupled with the physically-based hydrological model PROMET. To cover a possible range of uncertainties, 16 climate scenarios were taken as meteorological drivers which were defined from different ensemble outputs of a stochastic climate generator, based on the IPCC-SRES-A1B emission scenario and four regional climate trends. Depending on the trends, the results show a slight to severe decline in hydroelectric power generation. Whilst the mean summer values indicate a decrease, the mean winter values display an increase. To show past and future regional differences within the Upper Danube basin, three hydropower plants at individual locations were selected. Inter-annual differences originate predominately from unequal contributions of the runoff compartments rain, snow-and ice-melt

Modeling Human Visual Search Performance on Realistic Webpages Using Analytical and Deep Learning Methods

Modeling visual search not only offers an opportunity to predict the usability of an interface before actually testing it on real users, but also advances scientific understanding about human behavior. In this work, we first conduct a set of analyses on a large-scale dataset of visual search tasks on realistic webpages. We then present a deep neural network that learns to predict the scannability of webpage content, i.e., how easy it is for a user to find a specific target. Our model leverages both heuristic-based features such as target size and unstructured features such as raw image pixels. This approach allows us to model complex interactions that might be involved in a realistic visual search task, which can not be easily achieved by traditional analytical models. We analyze the model behavior to offer our insights into how the salience map learned by the model aligns with human intuition and how the learned semantic representation of each target type relates to its visual search performance.Comment: the 2020 CHI Conference on Human Factors in Computing System