scholarly journals Deficits in Implicit Attention to Social Signals in Schizophrenia and High Risk Groups: Behavioural Evidence from a New Illusion

2016 ◽  
pp. 163-184
Keyword(s):  
High Risk ◽  
Risk Groups ◽  
Social Signals ◽  
Behavioural Evidence

scholarly journals Deficits in Implicit Attention to Social Signals in Schizophrenia and High Risk Groups: Behavioural Evidence from a New Illusion

PLoS ONE ◽  
2009 ◽  
Vol 4 (5) ◽  
pp. e5581 ◽  
Author(s):  
Mascha van 't Wout ◽  
Sophie van Rijn ◽  
Tjeerd Jellema ◽  
René S. Kahn ◽  
André Aleman
Keyword(s):  
High Risk ◽  
Risk Groups ◽  
Social Signals ◽  
Behavioural Evidence

Suicide in Nógrád County, Hungary, 1970-1994

Crisis ◽  
1999 ◽  
Vol 20 (2) ◽  
pp. 64-70 ◽  
Author(s):  
Tamás Zonda
Keyword(s):  
High Risk ◽  
Psychiatric Disorders ◽  
Suicide Rate ◽  
Risk Groups ◽  
The Elderly ◽  
Female Ratio ◽  
Suicide Notes ◽  
Suicidal Intention ◽  
Male Female Ratio

The author examined completed suicides occurring over a period of 25 years in a county of Hungary with a traditionally low (relatively speaking) suicide rate of 25.8. The rates are clearly higher in villages than in the towns. The male/female ratio was close to 4:1, among elderly though only 1.5:1. The high risk groups are the elderly, divorced, and widowed. Violent methods are chosen in 66.4% of the cases. The rates are particularly high in the period April-July. Prior communication of suicidal intention was revealed in 16.3% of all cases. Previous attempts had been undertaken by 17%, which in turn means that 83% of suicides were first attempts. In our material 10% the victims left suicide notes. Psychiatric disorders were present in 60.1% of the cases, and severe, multiple somatic illnesses (including malignomas) were present in 8.8%. The majority of the data resemble those found in the literature.

A(H1N1)v cases of the 2009/2010 and 2010/2011 influenza seasons in the Medical and Health Sciences Centre of Debrecen University

2012 ◽  
Vol 153 (17) ◽  
pp. 649-654
Author(s):  
Piroska Orosi ◽  
Judit Szidor ◽  
Tünde Tóthné Tóth ◽  
József Kónya
Keyword(s):  
High Risk ◽  
Influenza Season ◽  
National Level ◽  
Organ Transplant ◽  
High Risk Patient ◽  
Risk Groups ◽  
Health Sciences ◽  
World Health ◽  
Transplant Patients ◽  
Health Organization

The swine-origin new influenza variant A(H1N1) emerged in 2009 and changed the epidemiology of the 2009/2010 influenza season globally and at national level. Aims: The aim of the authors was to analyse the cases of two influenza seasons. Methods: The Medical and Health Sciences Centre of Debrecen University has 1690 beds with 85 000 patients admitted per year. The diagnosis of influenza was conducted using real-time polymerase chain reaction in the microbiological laboratories of the University and the National Epidemiological Centre, according to the recommendation of the World Health Organization. Results: The incidence of influenza was not higher than that observed in the previous season, but two high-risk patient groups were identified: pregnant women and patients with immunodeficiency (oncohematological and organ transplant patients). The influenza vaccine, which is free for high-risk groups and health care workers in Hungary, appeared to be effective for prevention, because in the 2010/2011 influenza season none of the 58 patients who were administered the vaccination developed influenza. Conclusion: It is an important task to protect oncohematological and organ transplant patients. Orv. Hetil., 2012, 153, 649–654.

ON THE ROLE OF BIOMONITORING IN THE ASSESSMENT OF THE HEALTH OF THE POPULATION EXPOSED TO MERCURY

2017 ◽  
pp. 2-7
Author(s):  
L. V. Lukovnikova ◽  
G. I. Sidorin ◽  
L. A. Alikbaeva ◽  
A. V. Galochina
Keyword(s):  
High Risk ◽  
Biological Monitoring ◽  
Clinical Examination ◽  
Inorganic Compounds ◽  
Risk Groups ◽  
Comprehensive Approach ◽  
Chemical Monitoring ◽  
Environmental Objects

When examining the population exposed to organic and inorganic compounds of mercury, a comprehensive approach is proposed, including chemical monitoring of environmental objects, biological monitoring, clinical examination of persons exposed to mercury, identification of high-risk groups.

scholarly journals A Peri-Implant Disease Risk Score for Patients with Dental Implants: Validation and the Influence of the Interval between Maintenance Appointments

2019 ◽  
Vol 8 (2) ◽  
pp. 252 ◽  
Author(s):  
Miguel de Araújo Nobre ◽  
Francisco Salvado ◽  
Paulo Nogueira ◽  
Evangelista Rocha ◽  
Peter Ilg ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Disease Risk ◽  
Disease Incidence ◽  
Risk Groups ◽  
Risk Profile ◽  
Moderate Risk ◽  
C Statistic ◽  
Disease Risk Score ◽  
Very High

Background: There is a need for tools that provide prediction of peri-implant disease. The purpose of this study was to validate a risk score for peri-implant disease and to assess the influence of the recall regimen in disease incidence based on a five-year retrospective cohort. Methods: Three hundred and fifty-three patients with 1238 implants were observed. A risk score was calculated from eight predictors and risk groups were established. Relative risk (RR) was estimated using logistic regression, and the c-statistic was calculated. The effect/impact of the recall regimen (≤ six months; > six months) on the incidence of peri-implant disease was evaluated for a subset of cases and matched controls. The RR and the proportional attributable risk (PAR) were estimated. Results: At baseline, patients fell into the following risk profiles: low-risk (n = 102, 28.9%), moderate-risk (n = 68, 19.3%), high-risk (n = 77, 21.8%), and very high-risk (n = 106, 30%). The incidence of peri-implant disease over five years was 24.1% (n = 85 patients). The RR for the risk groups was 5.52 (c-statistic = 0.858). The RR for a longer recall regimen was 1.06, corresponding to a PAR of 5.87%. Conclusions: The risk score for estimating peri-implant disease was validated and showed very good performance. Maintenance appointments of < six months or > six months did not influence the incidence of peri-implant disease when considering the matching of cases and controls by risk profile.

scholarly journals SEROPREVALENCE OF LEPTOSPIROSIS IN HIGH RISK GROUPS IN CALICUT, NORTH KERALA, INDIA

2006 ◽  
Vol 24 (4) ◽  
pp. 349-352
Author(s):  
RN Swapna ◽  
U Tuteja ◽  
L Nair ◽  
J Sudarsana
Keyword(s):  
High Risk ◽  
Risk Groups

PET Parameters are Useful in Predicting Endometrial Cancer Risk Classes and Prognosis

2020 ◽  
Author(s):  
Adnan Budak ◽  
Emrah Beyan ◽  
Abdurrahman Hamdi Inan ◽  
Ahkam Göksel Kanmaz ◽  
Onur Suleyman Aldemir ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Myometrial Invasion ◽  
Risk Groups ◽  
Total Sample ◽  
Tumor Stage ◽  
Low Risk ◽  
Tumor Diameter ◽  
Grade 3 ◽  
Fdg Pet Ct

Abstract Aim We investigate the role of preoperative PET parameters to determine risk classes and prognosis of endometrial cancer (EC). Methods We enrolled 81 patients with EC who underwent preoperative F-18 FDG PET/CT. PET parameters (SUVmax, SUVmean, MTV, TLG), grade, histology and size of the primary tumor, stage of the disease, the degree of myometrial invasion (MI), and the presence of lymphovascular invasion (LVI), cervical invasion (CI), distant metastasis (DM) and lymph node metastasis (LNM) were recorded. The relationship between PET parameters, clinicopathological risk factors and overall survival (OS) was evaluated. Results The present study included 81 patients with EC (mean age 60). Of the total sample, 21 patients were considered low risk (endometrioid histology, stage 1A, grade 1 or 2, tumor diameter < 4 cm, and LVI negative) and 60 were deemed high risk. All of the PET parameters were higher in the presence of a high-risk state, greater tumor size, deep MI, LVI and stage 1B-4B. MTV and TLG values were higher in the patients with non-endometrioid histology, CI, grade 3 and LNM. The optimum cut-off levels for differentiating between the high and low risk patients were: 11.1 for SUVmax (AUC = 0.757), 6 for SUVmean (AUC = 0.750), 6.6 for MTV(AUC = 0.838) and 56.2 for TLG(AUC = 0.835). MTV and TLG values were found as independent prognostic factors for OS, whereas SUVmax and SUVmean values were not predictive. Conclusions The PET parameters are useful in noninvasively differentiating between risk groups of EC. Furthermore, volumetric PET parameters can be predictive for OS of EC.

scholarly journals Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Castela Forte ◽  
Galiya Yeshmagambetova ◽  
Maureen L. van der Grinten ◽  
Bart Hiemstra ◽  
Thomas Kaufmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Mortality Risk ◽  
Clustering Algorithms ◽  
Kidney Injury ◽  
Risk Groups ◽  
Admission Diagnosis ◽  
Dutch Hospital ◽  
Cluster Membership

AbstractCritically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25–56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

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