scholarly journals The Paradox of Safe Areas in Ethnic Civil Wars

2018 ◽  
Vol 10 (3) ◽  
pp. 362-386 ◽  
Author(s):  
Stefano Recchia
Keyword(s):  
At Risk ◽  
High Risk ◽  
Civil Wars ◽  
Medium Term ◽  
Short Term ◽  
Conflict Escalation ◽  
Civilian Protection ◽  
Causal Mechanisms ◽  
Peace Negotiations ◽  
Conflict Zone

Safe areas established by powerful states can improve short-term civilian protection during ethnic civil wars. Paradoxically, however, they may worsen the plight of vulnerable civilians over the medium term. This can occur in three ways. First, when safe areas encompass sizeable territories within a broader conflict zone, they may reduce incentives for protected groups to compromise during peace negotiations, thus prolonging hostilities. Second, there is a nontrivial possibility that protected groups will use the safe areas as a base for launching high-risk offensives, deliberately putting civilians at risk in the hope of drawing the protection forces more deeply into the war. Third, safe areas may embolden protected groups to seek unilateral secession, further increasing the risk of conflict escalation. By elucidating the causal mechanisms involved, this article helps us assess the probability of these outcomes occurring. States that consider intervening militarily to establish safe areas in ethnic civil wars need to weigh the short-term benefits against these possible longer-term downsides.

Outcomes among patients with cancer previously identified as being at risk for 30-day mortality using augmented intelligence.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 12031-12031
Author(s):  
Ajeet Gajra ◽  
Marjorie E. Zettler ◽  
Amy R. Ellis ◽  
Kelly A. Miller ◽  
John G. Frownfelter ◽  
...  
Keyword(s):  
At Risk ◽  
High Risk ◽  
Hospital Admissions ◽  
Short Term ◽  
Patients With Cancer ◽  
Short Term Mortality ◽  
Medium Risk ◽  
Ed Visits ◽  
Oncology Practice

12031 Background: An augmented intelligence (AI) tool using a machine learning algorithm was developed and validated to generate insights into risk for short-term mortality among patients with cancer. The algorithm, which scores patients every week as being at low, medium or high risk for death within 30 days, allowing providers to potentially intervene and modify care of those at medium to high risk based on established practice pathways. Deployment of the algorithm increased palliative care referrals in a large community hematology/oncology practice in the United States (Gajra et al, JCO 2020). The objective of this retrospective analysis was to evaluate the differences in survival and healthcare utilization (HCU) outcomes of patients previously scored as medium or high risk by the AI tool. Methods: Between 6/2018 – 10/2019, the AI tool scored patients on a weekly basis at the hematology/oncology practice. In 9/2020, a chart review was conducted for the 886 patients who had been identified by the algorithm as being at medium or high risk for 30-day mortality during the index period, to determine outcomes (including death, emergency department [ED] visits, and hospital admissions). Data are presented using descriptive statistics. Results: Of the 886 at-risk patients, 450 (50.8%) were deceased at the time of follow-up. Of these, 244 (54.2%) died within the first 180 days of scoring as at-risk, with median time to death 68 days (IQR 99). Among the 255 patients scored as high risk, 171 (67.1%) had died, vs. 279 (44.2%) of the 631 patients who were scored as medium risk (p < 0.001). Of the 601 patients who were scored more than once during the index period as medium or high risk, 342 (56.9%) had died, vs. 108 (37.9%) of the 285 who were scored as at risk only once (p < 0.001). A total of 363 patients (43.1%) had at least 1 ED visit, and 346 patients (41.1%) had at least 1 hospital admission. There was no difference in the proportion of patients scored as high risk compared with those scored as medium risk in ED visits (104 of 237 [43.9%] vs. 259 of 605 [42.8%], p = 0.778) or hospital admissions (100 of 237 [42.2%] vs. 246 of 605 [40.7%], p = 0.684, respectively). Compared with patients scored as medium or high risk only once during the index period, patients who were scored as at-risk more than once had more ED visits (282 of 593 [47.6%] vs. 81 of 249 [32.5%], p < 0.001) and hospital admissions (269 of 593 [45.4%] vs. 77 of 249 [30.9%], p < 0.001). Conclusions: This follow-up study found that half of the patients identified as at-risk for short-term mortality during the index period were deceased, with greater likelihood associated with high risk score and being scored more than once. Over 40% had visited an ED or were admitted to hospital. These findings have important implications for the use of the algorithm to guide treatment discussions, prevent acute HCU and to plan ahead for end of life care in patients with cancer.

scholarly journals Optimal treatment strategies for coronary artery disease in patients with advanced kidney disease: a meta-analysis

2021 ◽  
Vol 12 ◽  
pp. 204062232110243
Author(s):  
Jingwen Yong ◽  
Jinfan Tian ◽  
Xin Zhao ◽  
Xueyao Yang ◽  
Haoran Xing ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
High Risk ◽  
Kidney Disease ◽  
Low Risk ◽  
Medium Term ◽  
Short Term ◽  
Artery Disease

Background: Coronary artery disease (CAD) is the leading cause of death in advanced kidney disease. However, its best treatment has not been determined. Methods: We searched PubMed and Cochrane databases and scanned references to related articles. Studies comparing the different treatments for patients with CAD and advanced CKD (estimated glomerular filtration rate <30 ml/min/1.73 m2 or dialysis) were selected. The primary result was all-cause death, classified according to the follow-up time: short-term (<1 month), medium-term (1 month-1 year), and long-term (>1 year). Results: A total of 32 studies were selected to enroll 84,498 patients with advanced kidney disease. Compared with medical therapy (MT) alone, percutaneous coronary intervention (PCI) was associated with low risk of short-, medium-term and long-term all-cause death (more than 3 years). For AMI patients, compared with MT, PCI was not associated with low risk of short- and medium-term all-cause death. For non-AMI patients, compared with MT, PCI was associated with low risk of long-term mortality (more than 3 years). Compared with MT, coronary artery bypass surgery (CABG) had no significant advantages in each follow-up period of all-cause death. Compared with PCI, CABG was associated with a high risk of short-term death, but low risk of long-term death: 1–3 years; more than 3 years. CABG could also reduce the risk of long-term risk of cardiac death, major adverse cardiovascular events (MACEs), myocardial infarction (MI), and repeat revascularization. Conclusions: In patients with advanced kidney disease and CAD, PCI reduced the risk of short-, medium- and long- term (more than 3 years) all-cause death compared with MT. Compared with PCI, CABG was associated with a high risk of short-term death and a low risk of long-term death and adverse events.

Derivation and implementation of a machine learning approach to prompt serious illness conversations among outpatients with cancer.

2019 ◽  
Vol 37 (31_suppl) ◽  
pp. 131-131
Author(s):  
Ravi Bharat Parikh ◽  
Chris Manz ◽  
Corey Chivers ◽  
Susan B Regli ◽  
Jennifer Braun ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
High Risk ◽  
Risk Group ◽  
Gradient Boosting ◽  
Short Term ◽  
Serious Illness ◽  
Patients With Cancer ◽  
Short Term Mortality ◽  
The Impact

131 Background: Machine learning (ML) algorithms can accurately identify patients with cancer at risk of short-term mortality and facilitate timely conversations about treatment and end-of-life preferences. We developed, validated, and implemented a ML algorithm to predict mortality in a general oncology setting, using electronic health record (EHR) data prior to a clinic visit. Methods: Our cohort consisted of patients aged ≥18 years who had an encounter in outpatient oncology practices within a large academic health system between February 1st and July 1st, 2016. We randomly split the sample into training (70%) and validation (30%) cohorts at the patient-encounter level. We trained three ML algorithms to predict 180-day mortality and describe performance in the holdout validation cohort. From October 2018 to February 2019, we used the best-performing algorithm to generate weekly lists of high-risk patients at a single community oncology practice and studied the impact on rates of documented serious illness conversations (SICs). Results: Among 62,377 encounters used to train the algorithms, 7.4% involved a patient who died within 180 days. Gradient boosting and/or random forest outperformed logistic regression in all metrics (Table), and the gradient boosting model had superior discrimination and calibration. In the gradient boosting model, observed 180-day mortality was 45.5% (95% CI 39.0-52.3%) in the high-risk group vs. 3.3% (95% CI 2.9-3.7%) in the low-risk group. In a survey of oncology clinicians, 59% of patients flagged as high-risk were appropriate for a serious illness conversation in the upcoming week (response rate 52%). Five months after implementing the intervention, average monthly documented SICs increased by 23% (31.7 to 39). Conclusions: A ML algorithm based on EHR data accurately identified patients with cancer at risk of short-term mortality, was concordant with oncologists’ assessments, and was associated with more SICs. [Table: see text]

Is money the creation of the state or the market? (On the “modern monetary theory” as described in the textbook by W. Mitchell, L. R. Wray and M. Watts “Macroeconomics”)

2020 ◽  
pp. 121-134
Author(s):  
S. A. Andryushin
Keyword(s):  
Public Debt ◽  
Scientific Community ◽  
Full Employment ◽  
Term Effect ◽  
Scientific Concept ◽  
Monetary Theory ◽  
Left Wing ◽  
Medium Term ◽  
Short Term ◽  
Stimulation Of

In 2019, a textbook “Macroeconomics” was published in London, on the pages of which the authors presented a new monetary doctrine — Modern Monetary Theory, MMT, — an unorthodox concept based on the postulates of Post-Keynesianism, New Institutionalism, and the theory of Marxism. The attitude to this scientific concept in the scientific community is ambiguous. A smaller part of scientists actively support this doctrine, which is directly related to state monetary and fiscal stimulation of full employment, public debt servicing and economic growth. Others, the majority of economists, on the contrary, strongly criticize MMT, arguing that the new theory hides simple left-wing populism, designed for a temporary and short-term effect. This article considers the origins and the main provisions of MMT, its discussions with the mainstream, criticism of the basic tenets of MMT, and also assesses possible prospects for the development of MMT in the medium term.

scholarly journals Medium-term health and social outcomes in adolescents following sexual assault: a prospective mixed-methods cohort study

2021 ◽  
Author(s):  
Venetia Clarke ◽  
Andrea Goddard ◽  
Kaye Wellings ◽  
Raeena Hirve ◽  
Marta Casanovas ◽  
...  
Keyword(s):  
Mental Health ◽  
At Risk ◽  
Cohort Study ◽  
Mixed Methods ◽  
Sexual Assault ◽  
Young People ◽  
Stress Disorders ◽  
Social Outcomes ◽  
Study Entry ◽  
Medium Term

Abstract Purpose To describe medium-term physical and mental health and social outcomes following adolescent sexual assault, and examine users’ perceived needs and experiences. Method Longitudinal, mixed methods cohort study of adolescents aged 13–17 years recruited within 6 weeks of sexual assault (study entry) and followed to study end, 13–15 months post-assault. Results 75/141 participants were followed to study end (53% retention; 71 females) and 19 completed an in-depth qualitative interview. Despite many participants accessing support services, 54%, 59% and 72% remained at risk for depressive, anxiety and post-traumatic stress disorders 13–15 months post-assault. Physical symptoms were reported more frequently. Persistent (> 30 days) absence from school doubled between study entry and end, from 22 to 47%. Enduring mental ill-health and disengagement from education/employment were associated with psychosocial risk factors rather than assault characteristics. Qualitative data suggested inter-relationships between mental ill-health, physical health problems and disengagement from school, and poor understanding from schools regarding how to support young people post-assault. Baseline levels of smoking, alcohol and ever drug use were high and increased during the study period (only significantly for alcohol use). Conclusion Adolescents presenting after sexual assault have high levels of vulnerability over a year post-assault. Many remain at risk for mental health disorders, highlighting the need for specialist intervention and ongoing support. A key concern for young people is disruption to their education. Multi-faceted support is needed to prevent social exclusion and further widening of health inequalities in this population, and to support young people in their immediate and long-term recovery.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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