Spanish Validation of the Child Pornography Offender Risk Tool

Sexual Abuse ◽  
2020 ◽  
pp. 107906322092895
Author(s):  
Virginia Soldino ◽  
Enrique J. Carbonell-Vayá ◽  
Kathryn C. Seigfried-Spellar
Keyword(s):  
Sexual Interest ◽  
Child Pornography ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Preliminary Evidence ◽  
Recidivism Rate ◽  
Recidivism Rates ◽  
Cultural Validity ◽  
Sexual Offenses ◽  
Spanish Validation

The current study examined the validity of the Child Pornography Offender Risk Tool (CPORT) in a sample of 304 men arrested in Spain for child pornography (CP) offenses, distinguishing between CP-exclusive offenders ( n = 255) and CP offenders with other criminal involvement ( n = 49). In our 5-year fixed follow-up analysis, we observed a 2.3% sexual recidivism rate for the whole sample (2.0% new CP offenses, 0.3% new contact sexual offenses). Receiver operating characteristic (ROC) analyses detected some relative predictive ability of the CPORT for CP recidivism outcomes when the Correlates of Admission of Sexual Interest in Children (CASIC) was used to replace missing CPORT Item 5. Specifically, both CPORT and CASIC total scores might help predict new CP offending among CP-exclusive offenders (area under the curve [AUC] = .57 and .70, respectively). Calibration analyses found that the observed recidivism rates were much lower than the expected recidivism rates presented by the tool developers, and, thus, suggest caution over the use of these norms for applied risk assessment. Our findings provide, to some extent, preliminary evidence of CPORT cross-cultural validity.

Child Sexual Exploitation Materials Offenders

2018 ◽  
Vol 23 (2) ◽  
pp. 130-143 ◽  
Author(s):  
Kelly M. Babchishin ◽  
Hannah L. Merdian ◽  
Ross M. Bartels ◽  
Derek Perkins
Keyword(s):  
Sexual Offenders ◽  
Sexual Interest ◽  
Child Pornography ◽  
Sexual Exploitation ◽  
Criminal History ◽  
Risk Level ◽  
Sexual Offenses ◽  
Child Sexual Exploitation ◽  
Increased Risk ◽  
Antisocial Tendencies

Abstract. The downloading and possession of Child Sexual Exploitation Materials (CSEM; also referred to as child pornography and indecent images of children) is a commonly convicted type of Internet sexual offenses. This review summarizes the current state of knowledge on CSEM offenders. We first provide a summary of the key motivations of CSEM offenders, characteristics of CSEM offenders compared to contact sexual offenders against children, and important facilitative factors. We then review the factors related to recidivism among CSEM offenders. Finally, we describe current developments in the risk assessment, police case prioritization, and treatment approaches for CSEM offenders. Generally, CSEM offenders hold a sexual interest in children, are low on antisocial tendencies, and pose a low risk to reoffend (including contact sexual offending). Key facilitative factors for CSEM offending include access to children, offense-supportive cognitions, and sexual arousal. Factors indicative of antisocial tendencies (e.g., criminal history) are associated with an increased risk of reoffending. Lastly, we address atypical sexual interest, socio-affective dysfunctions, and strategies for maintaining an offense-free lifestyle as key treatment targets for CSEM offenders. Lower treatment dosage, however, should be considered given CSEM-exclusive offenders’ lower risk level for contact sexual offenses. We hope that this review will inspire others to explore the current research gaps in future studies.

scholarly journals Predictive Analytic Techniques to Identify Hidden Relationships between Training Load, Fatigue and Muscle Strains in Young Soccer Players

Sports ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Mauro Mandorino ◽  
António J. Figueiredo ◽  
Gianluca Cima ◽  
Antonio Tessitore
Keyword(s):  
Area Under The Curve ◽  
Predictive Ability ◽  
Peak Height ◽  
Neuromuscular Fatigue ◽  
Training Load ◽  
Soccer Players ◽  
Height Velocity ◽  
Support Vector ◽  
Peak Height Velocity ◽  
Injury Surveillance System

This study aimed to analyze different predictive analytic techniques to forecast the risk of muscle strain injuries (MSI) in youth soccer based on training load data. Twenty-two young soccer players (age: 13.5 ± 0.3 years) were recruited, and an injury surveillance system was applied to record all MSI during the season. Anthropometric data, predicted age at peak height velocity, and skeletal age were collected. The session-RPE method was daily employed to quantify internal training/match load, and monotony, strain, and cumulative load over the weeks were calculated. A countermovement jump (CMJ) test was submitted before and after each training/match to quantify players’ neuromuscular fatigue. All these data were used to predict the risk of MSI through different data mining models: Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM). Among them, SVM showed the best predictive ability (area under the curve = 0.84 ± 0.05). Then, Decision tree (DT) algorithm was employed to understand the interactions identified by the SVM model. The rules extracted by DT revealed how the risk of injury could change according to players’ maturity status, neuromuscular fatigue, anthropometric factors, higher workloads, and low recovery status. This approach allowed to identify MSI and the underlying risk factors.

scholarly journals Evaluating Selection for Sexually Violent Predator Commitment: A Comparison of Those Committed, Not Committed, and Nearly Committed

2020 ◽  
Author(s):  
Anthony Perillo ◽  
Cynthia Calkins ◽  
Elizabeth Jeglic
Keyword(s):  
Risk Scores ◽  
Sexual Recidivism ◽  
Multinomial Regression ◽  
Recidivism Rates ◽  
Sexually Violent Predator ◽  
Sexual Offenses ◽  
Empirically Supported ◽  
Historical Factors ◽  
Sexually Violent ◽  
Selection For

We examined state-wide data of persons evaluated for Sexually Violent Predator (SVP) commitment and compared risk-relevant data of three groups: those committed as SVPs ( n = 374), those not recommended for commitment ( n = 2,707), and those nearly committed (recommended for commitment but ultimately not committed; n = 117). Consistent with legal language for SVP commitment, binary and multinomial regression analyses revealed risk scores predicted SVP commitment recommendations in addition to some historical factors (e.g., psychiatric history, never being married, prior sex offenses). For those recommended for commitment, prior sexual offenses predicted ultimate commitment. Those nearly committed had significantly higher sexual recidivism rates than others who were not committed; however, these recidivism rates were still low (11.5%). Findings suggest evaluators’ SVP decisions incorporate risk data and follow empirically supported trends; however, the observed recidivism rates of a subset of those SVP commitment appears to target suggests SVP commitment’s potential for reducing sexual recidivism effectively and efficiently appears to have a low ceiling.

scholarly journals A Rationale for Using Sonoelastography in Thyroid Nodular Pathology

2019 ◽  
Vol 100 (5) ◽  
pp. 242-246
Author(s):  
L. A. Timofeeva ◽  
L. B. Shubin
Keyword(s):  
Thyroid Cancer ◽  
Roc Analysis ◽  
Thyroid Nodules ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Tissue Elasticity ◽  
Stromal Component ◽  
Collagen Level ◽  
Probable Nature ◽  
Benign Nodules

Objective. To provide a rationale for using sonoelastography (SEG) in the differential diagnosis of thyroid cancer (TC).Material and methods. Thirty patients with thyroid nodules of various morphological structures were examined. The authors studied the data of SEG and immunohistochemistry (IHC) with monoclonal antibodies against types III and IV collagen (they evaluated the degree of the expressed collagen fibers). Analysis of variance, ROC analysis, and logistic regression were used (by comparing with the expression of collagens) to assess the predictive ability of ultrasound.Results. The study showed that irregular and uneven contours, microcalcifications, and “the height greater than the width” were most significant among the ultrasound signs in the diagnosis of TC. Cool colors prevailed when performing SEG in the pattern of thyroid cancer. Purple-blue hues were predominantly recorded (p<0.05 with regard to benign nodules), green ones were less frequently. ROC analysis of compression elastography showed that the area under the curve was 0.785 (95% CI 0.740-0.826), sensitivity 78.1%, specificity 79.0%. Comparison of the data of IHC and SEG revealed a direct correlation of tissue elasticity with the degree of a stromal component and with the presence of collagen-containing structures.Conclusion. SEG may suppose the probable nature of thyroid nodules on the basis of their morphological features. The low degree of the stromal component and the low content of types III and IV collagen make follicular colloid goiter and adenoma soft, which is recorded at SEG. TC is characterized by a high collagen level attributable to the characteristics of the metabolism of cancer cells, which makes them solid in the mode of SEG.

scholarly journals Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Tenghui Han ◽  
Jun Zhu ◽  
Xiaoping Chen ◽  
Rujie Chen ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Predictive Model ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Subsequent Treatment ◽  
Training Dataset ◽  
Discriminative Ability ◽  
Fundamental Parameters

Abstract Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

scholarly journals Validation of Predictive Ability of Bobi Score in Burn Patients

2021 ◽  
Vol 6 (4) ◽  
Keyword(s):  
Burn Injury ◽  
Prognostic Score ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Scoring Systems ◽  
Good Prediction ◽  
Burn Patients ◽  
Severe Burns ◽  
Inhalation Burn ◽  
Outstanding Result

Introduction: Scoring systems have been used successfully in burn centers to predict the prognosis and take measures for careful monitoring of the burned patient. Belgium Outcome Burn Injury score is one of them which takes into consideration age, burn surface area, and presence of inhalation burn. Objectives: This presentation aims to validate the use of the BOBI prognostic score in our patients. Patients and Methods: The study is a retrospective analytical study that utilized the investigation of the medical charts of 1515 patients hospitalized with severe burns within the ICU of the Service of Burns in Tirana, Albania during 2010-2019. Results: The overall mortality of our patients was 7.06% (107 deaths in 1515 patients). Up to BOBI score 6, we have noticed better mortality than prediction while there is a very good prediction up to score 10. Area Under the Curve was 0.978 (p<0.0001) which is an outstanding result in being a classifier between deaths and survivors. Conclusions: BOBI score is a very good prediction score for mortality in burn patients.

Predicting Sexual Offenders’ Specialization/Versatility: The Role of Impulsivity and Moral Reasoning

Sexual Abuse ◽  
2019 ◽  
Vol 32 (8) ◽  
pp. 986-1011
Author(s):  
Mariana A. Saramago ◽  
Jorge Cardoso ◽  
Isabel Leal
Keyword(s):  
Linear Regression ◽  
Moral Reasoning ◽  
Sexual Offenders ◽  
Exploratory Study ◽  
Diversity Index ◽  
Predictive Ability ◽  
Criminal History ◽  
Regression Analyses ◽  
Sexual Offenses

The main purpose of this exploratory study was to examine the predictive ability of impulsivity and moral reasoning on offending specialization/versatility. The latter was measured using the diversity index which calculates the amount of variation within an individual’s criminal history. The sample consisted of 88 individuals convicted of sexual offenses incarcerated in a Portuguese prison. Group comparisons and multiple linear regression analyses on untransformed and corrected versions of the diversity index were conducted. Overall, the different versions of the diversity index presented disparate results. Individuals were found to be generally alike, but those convicted of rape tended to be more versatile than those who molested extrafamilial children. Moral reasoning was the strongest predictor of offending specialization/versatility, while impulsivity was mostly not statistically significant. A better understanding of these predictors’ roles on offending specialization/versatility, as it relates to recidivism, is important to tailor successful interventions.

Cognitive-Behavioral Therapy Efficacy for Reducing Recidivism Rates of Moderate- and High-Risk Sexual Offenders: A Scoping Systematic Literature Review

2016 ◽  
Vol 62 (1) ◽  
pp. 170-186 ◽  
Author(s):  
Elias Mpofu ◽  
James A. Athanasou ◽  
Christine Rafe ◽  
Scott H. Belshaw
Keyword(s):  
High Risk ◽  
Cognitive Behavioral Therapy ◽  
Sexual Offenders ◽  
Behavioral Therapy ◽  
Cognitive Behavioral ◽  
Future Research ◽  
Sexual Offense ◽  
Recidivism Rate ◽  
Recidivism Rates ◽  
Sample Median

This literature scoping review compared recidivism rates of moderate- and high-risk sexual offenders who received cognitive-behavioral therapy (CBT) oriented treatments. Ten empirical studies from 2001 to 2014 were selected for review that met the following criteria: (a) Treatment program included a CBT-based intervention with a comparative intervention; (b) participants included adult, male, moderate- and high-risk sexual offenders only; and (c) follow-up data for up to 12 months. Data were analyzed using a summative metric for recidivism rate comparisons ( N = 3,073 for CBT and N = 3,588, for comparison approaches). Sexual offense recidivism rates varied from 0.6% to 21.8% (with CBT) and from 4.5% to 32.3% (with comparison intervention). The within-sample median rate of violent recidivism with a history of sexual offense was 21.1% (with CBT) versus 32.6% (comparison). Sexual offenders had a general felonies (within-sample) median recidivism rate of 27.05% (with CBT) versus 51.05% (comparison). The evidence supports the conclusion that CBT in its various forms is an efficacious treatment modality to prevent offense recidivism by sexual offenders. Suggestions for future research are considered.

Recidivism

2018 ◽  
Author(s):  
Faye S. Taxman ◽  
Mary Mun
Keyword(s):  
Justice System ◽  
Drug Users ◽  
Recidivism Rate ◽  
Recidivism Rates ◽  
Factors Affecting ◽  
Drug Of Choice ◽  
Drugs And Crime ◽  
Time Frames ◽  
The Relationship

High rates of rearrest and recidivism, especially among drug-involved individuals, are of grave concern for the justice system and society at large. This chapter looks at the factors affecting recidivism rates of substance-involved individuals involved in the justice system. We begin by considering the complexity of measuring recidivism and the meaning of this concept; the term is fraught with difficulties due to the complexities of generalizing findings across studies with varying sampling frames and time-frames for follow-up, and differences in the types of recidivism events studied. Recent research illustrates that recidivism rates among drug users vary by drug of choice and are typically higher among individuals who use amphetamines, heroin, and/or cocaine. Recidivism rates may also vary depending on the presence of certain comorbid factors, although this is an emerging area of research. Factors that appear to elevate recidivism rates include personality disorders, co-occurring substance abuse and mental health disorders, other psychiatric disorders, and other serious mental illness. The location of an individual’s residence also appears to impact the recidivism rate, possibly mediated by the presence or absence of various protective factors in the community. While the nature of the relationship between drugs and crime is still unclear, the same is true for our understanding of recidivism among substance users in the justice system. There is a need for a greater understanding of the relationship between substance use and recidivism, in order to fill existing knowledge gaps.

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