scholarly journals Risk Factors for Addiction and Their Association with Model-Based Behavioral Control

2016 ◽  
Vol 10 ◽  
Author(s):  
Andrea M. F. Reiter ◽  
Lorenz Deserno ◽  
Tilmann Wilbertz ◽  
Hans-Jochen Heinze ◽  
Florian Schlagenhauf
Keyword(s):  
Risk Factors ◽  
Behavioral Control ◽  
Model Based

scholarly journals Predicting Damage to Hop Cones by Tetranychus urticae (Acari: Tetranychidae)

2021 ◽  
Author(s):  
Joanna L Woods ◽  
Anne E Iskra ◽  
David H Gent
Keyword(s):  
Risk Factors ◽  
Tetranychus Urticae ◽  
Crop Damage ◽  
Equation Modeling ◽  
Data Sets ◽  
Current Management ◽  
Late Season ◽  
Model Based ◽  
Predatory Arthropods ◽  
Cone Damage

Abstract Twospotted spider mite (Tetranychus urticae Koch) is a cosmopolitan pest of numerous plants, including hop (Humulus lupulus L.). The most costly damage from the pest on hop results from infestation of cones, which are the harvested product, which can render crops unsalable if cones become discolored. We analyzed 14 yr of historical data from 312 individual experimental plots in western Oregon to identify risk factors associated with visual damage to hop cones from T. urticae. Logistic regression models were fit to estimate the probability of cone damage. The most predictive model was based on T. urticae-days during mid-July to harvest, which correctly predicted occurrence and nonoccurrence of cone damage in 91 and 93% of data sets, respectively, based on Youden’s index. A second model based on the ratio of T. urticae to predatory arthropods late in the season correctly predicted cone damage in 92% of data sets and nonoccurrence of damage in 77% of data sets. The model based on T. urticae abundance performed similarly when validated in 23 commercial hop yards, whereas the model based on the predator:prey ratio was relatively conservative and yielded false-positive predictions in 11 of the 23 yards. Antecedents of these risk factors were explored and quantified by structural equation modeling. A simple path diagram was constructed that conceptualizes T. urticae invasion of hop cones as dependent on prior density of the pest on leaves in early spring and summer, which in turn influences the development of predatory arthropods that mediate late-season density of the pest. In summary, the biological insights and models developed here provide guidance to pest managers on the likelihood of visual cone damage from T. urticae that can inform late-season management based on both abundance of the pest and its important predators. This is critically important because a formal economic threshold for T. urticae on hop does not exist and current management efforts may be mistimed to influence the pest when crop damage is most probable. More broadly, this research suggests that current management practices that target T. urticae early in the season may in fact predispose yards to later outbreaks of the pest.

Development of a Multivariable Model Based on Individual Risk Factors for Recurrent Lateral Patellar Dislocation

2021 ◽  
Vol 103 (7) ◽  
pp. 586-592
Author(s):  
Daphne I. Ling ◽  
Jacqueline M. Brady ◽  
Elizabeth Arendt ◽  
Marc Tompkins ◽  
Julie Agel ◽  
...  
Keyword(s):  
Risk Factors ◽  
Patellar Dislocation ◽  
Individual Risk ◽  
Multivariable Model ◽  
Model Based ◽  
Lateral Patellar Dislocation ◽  
Individual Risk Factors

Risk factors and geospatial modelling for the presence of Fasciola hepatica infection in sheep and goat farms in the Greek temperate Mediterranean environment

Parasitology ◽  
2011 ◽  
Vol 138 (7) ◽  
pp. 926-938 ◽  
Author(s):  
V. KANTZOURA ◽  
M. K. KOUAM ◽  
N. DEMIRIS ◽  
H. FEIDAS ◽  
G. THEODOROPOULOS
Keyword(s):  
Risk Factors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fasciola Hepatica ◽  
Small Ruminants ◽  
Endemic Region ◽  
Pasture Management ◽  
Entire Area ◽  
Model Based ◽  
Geospatial Modelling

SUMMARYRisk factors related to herd and farmer status, farm and pasture management, and environmental factors derived by satellite data were examined for their association with the prevalence of F. hepatica in sheep and goat farms in Thessaly, Greece. Twelve farms (16·2%) and 58 farms (78·4%) of 74 had evidence of infection using coproantigen and serology respectively. The average normalized difference vegetation index (NDVI) of farm location for 12 months before sampling was the most significant environmental risk factor for F. hepatica infection based on high seropositivity. The risk of infection increased by 1% when the value of NDVI increased by 0·01 degree. A geospatial map was constructed to show the relative risk (RR) of Fasciola infection in sheep and goat farms in Thessaly. In addition, geospatial maps of the model-based predicted RR for the presence of Fasciola infection in farms in Thessaly and the entire area of Greece were constructed from the developed model based on NDVI. In conclusion, this study demonstrated that Thessaly should be regarded as an endemic region for Fasciola infection and it represents the first prediction model of Fasciola infection in small ruminants in the Mediterranean basin.

Validation of model-based estimates (synthetic estimates) of the prevalence of risk factors for coronary heart disease for wards in England

2009 ◽  
Vol 15 (2) ◽  
pp. 596-605 ◽  
Author(s):  
Peter Scarborough ◽  
Steven Allender ◽  
Mike Rayner ◽  
Michael Goldacre
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Model Based

scholarly journals A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

2020 ◽  
Author(s):  
Xiaojun Ma ◽  
Huifang Wang ◽  
Junwei Huang ◽  
Yan Geng ◽  
Shuqi Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bootstrap Method ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Categorical Variables ◽  
P Value ◽  
Continuous Variables ◽  
Model Based ◽  
Independent Risk Factors ◽  
D Dimer

Abstract Background and Aim COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission. Methods COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the multivariate model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method. Results A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (odds ratio [OR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P<0.001), chronic heart disease (CHD, OR: 0.048, 95% CI: 0.013-0.180; P<0.001), the percentage of lymphocytes (Lym%, OR: 1.116, 95% CI: 1.051-1.184; P<0.001), platelets (OR: 1.008, 95% CI: 1.003-1.012; P=0.001), C-reaction protein (OR: 0.982, 95% CI: 0.973-0.991; P<0.001), lactate dehydrogenase (LDH, OR: 0.993, 95% CI: 0.990-0.997; P<0.001) and D-dimer (OR: 0.734, 95% CI: 0.615-0.875; P=0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973). Conclusion A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

scholarly journals An approach for the management of the risk factors impacting the model-based engineering methods in ERP projects

2018 ◽  
Vol 51 (11) ◽  
pp. 1206-1211 ◽  
Author(s):  
Sarra Mamoghli ◽  
Virginie Goepp ◽  
Valérie Botta-Genoulaz
Keyword(s):  
Risk Factors ◽  
Model Based

A prediction model for chemotherapy-associated thrombocytopenia in cancer patients

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 8616-8616 ◽  
Author(s):  
N. M. Kuderer ◽  
C. W. Francis ◽  
J. Crawford ◽  
D. C. Dale ◽  
D. A. Wolff ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Likelihood Ratio ◽  
Test Performance ◽  
Dose Intensity ◽  
High Specificity ◽  
Performance Characteristics ◽  
Multivariate Logistic Regression Model ◽  
Model Based ◽  
The Impact

8616 Background: Thrombocytopenia (TP) can lead to serious complications, however, little is known about the incidence and risk factors for chemotherapy-associated TP. A prospective, nationwide cohort study was undertaken to better define the impact of TP in cancer treatment. Methods: 2,842 patients with cancer of the breast, lung, colon, ovary or lymphoma initiating a new chemotherapy regimen have been prospectively enrolled at 115 randomly selected US community oncology practices between 2002 and 2005. Risk factors for chemotherapy-associated TP were identified, a multivariate logistic regression model based on pretreatment characteristics was developed, and test performance characteristics were estimated. Results: Over a median of 3 cycles of chemotherapy, minimum recorded platelet counts were: ≥150K in 53% of patients; 100–150K in 26%; 75–100K in 8%; 50–75K in 6% and <50K in 7%. Significant independent predictive factors for platelets <75K include type of cancer (P<.0001), type of chemotherapy including gemcitabine-based (P<.0001), anthracycline-based (P<.0001) and platinum-based (P<.0001) regimens, prior chemotherapy (P<.0001) or surgery (P=.005), age (P=.015), Caucasian ethnicity (P=.022), body surface area (P=.0001), planned relative dose intensity ≥85% (P=.082), diabetes (P=.018), pulmonary disease (P=.011), abnormal baseline platelets (P<.0001), hematocrit (P=0.030), alkaline phosphatase (P=.072) or albumin (P=.017). Model fit was good (Chi-square, P<.0001), R2 = 0.735 and c-statistic = 0.816 [95% CI: 0.792–0.840, P<.0001]. Model test performance characteristics [95% CI] at a ≥20% risk of TP include: sensitivity 56% [51–61]; specificity 88% [87–89]; likelihood ratio positive 4.63 [4.02–5.33]; likelihood ratio negative 0.50 [0.45–0.57]; and diagnostic odds ratio 9.22 [7.23–11.75]. Validation of the model is underway. Conclusions: This prediction model based on pretreatment factors identifies with high specificity patients at risk for clinically important chemotherapy-associated thrombocytopenia early in the treatment course. It may provide a valuable tool for guiding chemotherapy and new supportive care measures. [Table: see text]

International Real Estate Review

2007 ◽  
Vol 10 (2) ◽  
pp. 23-41
Author(s):  
Ping Cheng ◽  
◽  
Stephen E. Roulac ◽  
Keyword(s):  
Risk Factors ◽  
Real Estate ◽  
Return Predictability ◽  
Multifactor Model ◽  
Model Based ◽  
Specific Factors ◽  
High Consistency ◽  
The Relationship

This paper examines the relationship between return predictability and REIT characteristics. We build a multifactor model based on a set of firm-specific factors that include (1) Risk factors; (2) Liquidity factors; (3) Expensiveness; (4) Profitability; and (5) Return history. Our model demonstrates the capability of predicting the “winners” and the “losers,” with fairly high consistency. Given the large return differences uncovered by the model, and the fundamental characteristics of the “winners” versus the “losers,” it is unlikely that strong results are artifacts of a biased methodology.

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