Quadient (Netherlands)

BackgroundPain assessment is a necessary step in pain management in older people in palliative care. In older people, pain assessment can be challenging due to underreporting and atypical pain manifestations by other distressing symptoms. Anxiety, fatigue, loss of appetite, nausea, insomnia, dyspnoea, and bowel problems correlate with pain in palliative care patients. Insight into these symptoms as predictors may help to identify the underlying presence of pain. This study aimed to develop and test a prediction model for pain in community-dwelling frail older people in palliative care.MethodsIn this cross-sectional observational study, community-care nurses from multiple organizations across the Netherlands included eligible patients (life expectancy < 1 year, aged 65+, community-dwelling and frail). The outcome pain and symptoms were assessed by means of the Utrecht Symptom Diary. Also, demographic and illness information, including relevant covariates age, sex and living situation, was collected. Multivariable logistic regression and minimum Akaike Information Criterion(AIC) were used for model development and Receiver Operating Characteristics(ROC)-analysis for model performance. Additionally, predicted probability of pain are given for groups differing in age and sex.ResultsA total of 157 patients were included. The final model consisted of insomnia(Odds Ratio[OR] = 2.13, 95% Confidence Interval[CI] = 1.01–1.30), fatigue(OR = 3.47, 95% CI = 1.11–1.43), sex(female)(OR = 3.83, 95% CI = 2.11–9.81) and age(OR=-1.59, 95% CI = 0.92–1.01) as predicting variables. There is an overall decreasing trend for age, older persons suffer less from pain and females have a higher probability of experiencing pain. Model performance was indicated as fair with a sensitivity of 0.74(95% CI = 0.64–0.83) and a positive predictive value of 0.80(95% CI = 0.70–0.88).ConclusionsInsomnia and fatigue are predicting symptoms for pain, especially in women and younger patients. Further testing of the model in external cohorts is needed before clinical adoption.

• Ambition setting is essential for fostering transformations towards climate resilient futures. • Ambition setting connects envisioning futures with setting goals and actions to achieve them. • Climate services and tools can support ambition setting, illustrated by four examples. • Four key criteria for climate services and tools for ambition setting are defined. Climate change adaptation efforts need to accelerate and scale-up to deal with increasing climate change impacts worldwide in order to safeguard the resilience of societies. Currently adaptation action is merely following a risk-based planning approach, going from identifying a climate related risk to directly finding solutions. This has resulted into largely fragmented, local, and incremental adaptation actions up to present. There is a need for transformational change, and combining adaptation with other policy objectives, to speed up action towards climate resilient development. However, this integration alone may not be sufficient to address the systemic transformation required to tackle the root causes of existing challenges and underlaying vulnerabilities. A broader perspective is needed to envision the “future we want” and defining key goals and actions to achieve these futures. We believe that such an ambition setting process is critical, and commonly missing in adaptation planning. With ambition setting we mean a policy process that entails developing visions coupled with identifying goals and actions that work towards these visions. Ambition setting builds upon understanding the desired transformations in the system and the root cause of present challenges, including risks and vulnerabilities. To put ambition setting into practice climate services and tools can be employed. We identify key criteria supporting the selection of such tools and provide four examples showcasing how the tools support ambition setting. A tradition of ambition setting should be fostered, as well as tools and services should be further developed in parallel to accelerate transformations towards climate resilient development.

SQL Server has provided several options for high availability and disaster recovery. Just like with performing regular database backups to ensure you can revert to a previous state of your database should a crash or data corruption occur, planning and maintaining highly available database environments is part of the job of a database administrator (or site reliability engineer).

In the previous chapters, we spent significant time on how we can query data stored inside SQL Server instances or on HDFS through Spark. One advantage of having access to data stored in different formats is that it allows you to perform analysis of the data at a large, and distributed, scale. One of the more powerful options we can utilize inside Big Data Clusters is the ability to implement machine learning solutions on our data. Because Big Data Clusters allow us to store massive amounts of data in all kinds of formats and sizes, the ability to train, and utilize, machine learning models across all of that data becomes far easier.

Used internally by SQL Server to initialize the synchronization of the creation of an allocation ring buffer.

In the previous chapters, we spent significant time on how we can query data stored inside SQL Server Instances or on HDFS through Spark. One advantage of having access to data stored in different formats is that it allows you to perform analysis of the data at a large, and distributed, scale. One of the more powerful options we can utilize inside Big Data Clusters is the ability to implement machine learning solutions on our data. Because Big Data Clusters allows us to store massive amounts of data in all kinds of formats and sizes, the ability to train, and utilize, machine learning models across all of that data becomes far easier.

There have always been several options available within SQL Server to make sure your database is always available to your users and/or the data inside your database is replicated to another server so as to minimize the chances of losing data. Just like with performing regular database backups to ensure you can revert to a previous state of your database should a crash or data corruption occur, planning and maintaining highly available database environments is part of your job as a DBA.