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10.1016/S2215-0366(16)00103-6 http://scihub22266oqcxt.onion/10.1016/S2215-0366(16)00103-6 C4898588!4898588 !27086134     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27086134 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Lancet+Psychiatry 2016 ; 3 (6 ): 535-43 Nephropedia Template TP {{tp|p=27086134 |t=2016. Prediction of violent reoffending on release from prison: derivation and external validation of a scalable tool |pdf=|usr=}}{{27086134 }} gab.com Text Prediction of violent reoffending on release from prison: derivation and external validation of a scalable tool JO: Lancet Psychiatry http://www.kidney.de/pget.php?&tw=1&pmid=27086134 #FOAvip BACKGROUND: More than 30 million people are released from prison worldwide every year, who include a group at high risk of perpetrating interpersonal violence. Because there is considerable inconsistency and inefficiency in identifying those who would benefit from interventions to reduce this risk, we developed and validated a clinical prediction rule to determine the risk of violent offending in released prisoners. METHODS: We did a cohort study of a population of released prisoners in Sweden. Through linkage of population-based registers, we developed predictive models for violent reoffending for the cohort. First, we developed a derivation model to determine the strength of prespecified, routinely obtained criminal history, sociodemographic, and clinical risk factors using multivariable Cox proportional hazard regression, and then tested them in an external validation. We measured discrimination and calibration for prediction of our primary outcome of violent reoffending at 1 and 2 years using cutoffs of 10% for 1-year risk and 20% for 2-year risk. FINDINGS: We identified a cohort of 47?326 prisoners released in Sweden between 2001 and 2009, with 11?263 incidents of violent reoffending during this period. We developed a 14-item derivation model to predict violent reoffending and tested it in an external validation (assigning 37?100 individuals to the derivation sample and 10?226 to the validation sample). The model showed good measures of discrimination (Harrell's c-index 0·74) and calibration. For risk of violent reoffending at 1 year, sensitivity was 76% (95% CI 73-79) and specificity was 61% (95% CI 60-62). Positive and negative predictive values were 21% (95% CI 19-22) and 95% (95% CI 94-96), respectively. At 2 years, sensitivity was 67% (95% CI 64-69) and specificity was 70% (95% CI 69-72). Positive and negative predictive values were 37% (95% CI 35-39) and 89% (95% CI 88-90), respectively. Of individuals with a predicted risk of violent reoffending of 50% or more, 88% had drug and alcohol use disorders. We used the model to generate a simple, web-based, risk calculator (OxRec) that is free to use. INTERPRETATION: We have developed a prediction model in a Swedish prison population that can assist with decision making on release by identifying those who are at low risk of future violent offending, and those at high risk of violent reoffending who might benefit from drug and alcohol treatment. Further assessments in other populations and countries are needed. FUNDING: Wellcome Trust, the Swedish Research Council, and the Swedish Research Council for Health, Working Life and Welfare. Twit Text FOAVip Prediction of violent reoffending on release from prison: derivation and external validation of a scalable tool ????Lancet Psychiatry http://www.kidney.de/pget.php?&tw=1&pmid=27086134 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27086134 #FOAvip English Wikipedia <ref>{{Cite pmid|27086134 }}</ref> Prediction of violent reoffending on release from prison: derivation and external validation of a scalable tool #MMPMID27086134 Fazel S ; Chang Z ; Fanshawe T ; Långström N ; Lichtenstein P ; Larsson H ; Mallett S Lancet Psychiatry 2016[Jun]; 3 (6 ): 535-43 PMID27086134 show ga BACKGROUND: More than 30 million people are released from prison worldwide every year, who include a group at high risk of perpetrating interpersonal violence. Because there is considerable inconsistency and inefficiency in identifying those who would benefit from interventions to reduce this risk, we developed and validated a clinical prediction rule to determine the risk of violent offending in released prisoners. METHODS: We did a cohort study of a population of released prisoners in Sweden. Through linkage of population-based registers, we developed predictive models for violent reoffending for the cohort. First, we developed a derivation model to determine the strength of prespecified, routinely obtained criminal history, sociodemographic, and clinical risk factors using multivariable Cox proportional hazard regression, and then tested them in an external validation. We measured discrimination and calibration for prediction of our primary outcome of violent reoffending at 1 and 2 years using cutoffs of 10% for 1-year risk and 20% for 2-year risk. FINDINGS: We identified a cohort of 47?326 prisoners released in Sweden between 2001 and 2009, with 11?263 incidents of violent reoffending during this period. We developed a 14-item derivation model to predict violent reoffending and tested it in an external validation (assigning 37?100 individuals to the derivation sample and 10?226 to the validation sample). The model showed good measures of discrimination (Harrell's c-index 0·74) and calibration. For risk of violent reoffending at 1 year, sensitivity was 76% (95% CI 73-79) and specificity was 61% (95% CI 60-62). Positive and negative predictive values were 21% (95% CI 19-22) and 95% (95% CI 94-96), respectively. At 2 years, sensitivity was 67% (95% CI 64-69) and specificity was 70% (95% CI 69-72). Positive and negative predictive values were 37% (95% CI 35-39) and 89% (95% CI 88-90), respectively. Of individuals with a predicted risk of violent reoffending of 50% or more, 88% had drug and alcohol use disorders. We used the model to generate a simple, web-based, risk calculator (OxRec) that is free to use. INTERPRETATION: We have developed a prediction model in a Swedish prison population that can assist with decision making on release by identifying those who are at low risk of future violent offending, and those at high risk of violent reoffending who might benefit from drug and alcohol treatment. Further assessments in other populations and countries are needed. FUNDING: Wellcome Trust, the Swedish Research Council, and the Swedish Research Council for Health, Working Life and Welfare. |*Models, Statistical [MESH] |Adult [MESH] |Cohort Studies [MESH] |Crime/*statistics & numerical data [MESH] |Female [MESH] |Humans [MESH] |Male [MESH] |Middle Aged [MESH] |Prisoners/*psychology/statistics & numerical data [MESH] |Registries [MESH] |Reproducibility of Results [MESH] |Risk Factors [MESH] |Sweden [MESH]Prediction of violent reoffending on release from prison: derivation a(epmc).pdf DeepDyve Pubget Overpricing