Hydrogen Risk Assessment Models (HyRAM) Version 3.1 Technical Reference Manual.

SummaryVenous thromboembolism (VTE) in patients with cancer is associated with an increased morbidity and mortality, and its prevention is of major clinical importance. However, the VTE rates in the cancer population vary between 0.5% - 20%, depending on cancer-, treatment- and patient-related factors. The most important contributors to VTE risk are the tumor entity, stage and certain anticancer treatments. Cancer surgery represents a strong risk factor for VTE, and medical oncology patients are at increased risk of developing VTE, especially when receiving chemotherapy or immunomodulatory drugs. Also biomarkers have been investigated for their usefulness to predict risk of VTE (e.g. elevated leukocyte and platelet counts, soluble P-selectin, D-dimer, etc.). In order to identify cancer patients at high risk of VTE and to improve risk stratification, risk assessment models have been developed, which contain both clinical parameters and biomarkers. While primary thromboprophylaxis with lowmolecular- weight-heparin (LMWH) is recommended postoperatively for a period of up to 4 weeks after major cancer surgery, the evidence is less clear for medical oncology patients. Thromboprophylaxis in hospitalized medical oncology patients is advocated, and is based on results of randomized controlled trials which evaluated the efficacy and safety of LMWH for prevention of VTE in hospitalized medically ill patients. In recent trials the benefit of primary thromboprophylaxis in cancer patients receiving chemotherapy in the ambulatory setting has been investigated. However, at the present stage primary thromboprophylaxis for prevention of VTE in these patients is still a matter of debate and cannot be recommended for all cancer outpatients.

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Risk assessment models with functionally different influences on natural and technical systems

Technology of technosphere safety ◽

10.25257/tts.2020.3.89.8-19 ◽

2020 ◽

Vol 89 ◽

pp. 8-19

Author(s):

V. A. Minaev ◽

◽

N. G. Topolsky ◽

A. O. Faddeev ◽

R. O. Stepanov ◽

...

Keyword(s):

Risk Assessment ◽

Differential Equations ◽

Research Area ◽

Practical Implementation ◽

Model Assessment ◽

Mathematical Functions ◽

Effective Response ◽

Risk Assessment Models ◽

Technical Systems ◽

Material Values

Introduction. The complex combination of natural and technogenic factors that lead to dangerous threats to the health and life of the population, as well as to material values, creates a need to develop special mathematical models for risk assessment in the relevant territories. Herewith it is important to take into account the significant differences between these factors. The new areas of research are models that describe natural and technogenic risks using differential equations that reflect different types of functions. The article presents the development of this research area. Goals and objectives. The goal of the article is to create a model for risk assessment in natural and technical systems (PTS), based on taking into account the influences of different natural and technogenic factors on them. Objectives include justification, construction and practical implementation of the mathematical model of risk assessment in the form of differential equations system. Methods include interpretation of the considered influences on PTS in terms of risks and assessment of the dynamic interaction of natural and technogenic factors in the form of inhomogeneous differential equations. Results and discussion. Solutions for models of assessing complex natural and technogenic risks in relation to two cases that differ in NTS are found: functionally different external natural and technogenic influences on PTS, which are understood as their type, in which the effects of both natural and technogenic factors are described by different mathematical functions. Conclusions. The first model considers parabolic (reflecting threats whose intensity gradually decreases with distance from the epicenter) and linear types of influences (reflecting sudden threats). The second model considers parabolic and hyperbolic (reflecting threats, the intensity of which decreases sharply over time) types of influences. It is concluded that it is necessary to create a special computer album of complex influences on the PTS in order to prevent "replay" of various situations and develop the most effective response to emerging dangers from the EMERCOM units and other structures. Key words: model, assessment, natural and technogenic risks, functionally different influences, counteraction, EMERCOM units.

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A practical approach for evaluating the strength of knowledge supporting risk assessment models

Safety Science ◽

10.1016/j.ssci.2019.104596 ◽

2020 ◽

Vol 124 ◽

pp. 104596 ◽

Cited By ~ 1

Author(s):

Tasneem Bani-Mustafa ◽

Zhiguo Zeng ◽

Enrico Zio ◽

Dominique Vasseur

Keyword(s):

Risk Assessment ◽

Practical Approach ◽

Risk Assessment Models

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Layers of Bias: A Unified Approach for Understanding Problems With Risk Assessment

Criminal Justice and Behavior ◽

10.1177/0093854818811379 ◽

2018 ◽

Vol 46 (2) ◽

pp. 185-209 ◽

Cited By ~ 20

Author(s):

Laurel Eckhouse ◽

Kristian Lum ◽

Cynthia Conti-Cook ◽

Julie Ciccolini

Keyword(s):

Risk Assessment ◽

Criminal Justice ◽

Justice System ◽

Quantitative Risk Assessment ◽

Risk Scores ◽

Risk Models ◽

Risk Assessment Models ◽

Final Layer ◽

The Supreme Court ◽

Using Data

Scholars in several fields, including quantitative methodologists, legal scholars, and theoretically oriented criminologists, have launched robust debates about the fairness of quantitative risk assessment. As the Supreme Court considers addressing constitutional questions on the issue, we propose a framework for understanding the relationships among these debates: layers of bias. In the top layer, we identify challenges to fairness within the risk-assessment models themselves. We explain types of statistical fairness and the tradeoffs between them. The second layer covers biases embedded in data. Using data from a racially biased criminal justice system can lead to unmeasurable biases in both risk scores and outcome measures. The final layer engages conceptual problems with risk models: Is it fair to make criminal justice decisions about individuals based on groups? We show that each layer depends on the layers below it: Without assurances about the foundational layers, the fairness of the top layers is irrelevant.

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