scholarly journals A Study on Risk Classification System in Supply Chain

2012 ◽  
Vol 19D (3) ◽  
pp. 257-262
Author(s):  
Eun-Soo Kim ◽  
Byung-Jun Song ◽  
Jong-Yun Lee
Keyword(s):  
Supply Chain ◽  
Classification System ◽  
Risk Classification

scholarly journals Percepção de enfermeiros sobre utilização do protocolo do sistema de classificação de risco manchester/ Nurses’ perception on the use of the manchester risk classification system protocol

2015 ◽  
Vol 14 (2) ◽  
pp. 1004 ◽  
Author(s):  
Marcia Luciane da Silva Bohn ◽  
Maria Alice Dias da Silva Lima ◽  
Carmen Lúcia Mottin Duro ◽  
Kelly Piacheski de Abreu
Keyword(s):  
Classification System ◽  
Risk Classification

Supply chain risk classification: discussion and proposal

2014 ◽  
Vol 53 (22) ◽  
pp. 6868-6887 ◽  
Author(s):  
Djalma Araújo Rangel ◽  
Taiane Kamel de Oliveira ◽  
Maria Silene Alexandre Leite
Keyword(s):  
Supply Chain ◽  
Risk Classification ◽  
Supply Chain Risk

Predictive value of the new ESGO-ESTRO-ESP endometrial cancer risk classification on survival and recurrence in the Danish population

2021 ◽  
pp. ijgc-2021-002582
Author(s):  
Gitte Ortoft ◽  
Claus Høgdall ◽  
Estrid Stæhr Hansen ◽  
Margit Dueholm
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Recurrence Rate ◽  
European Society ◽  
Classification System ◽  
Risk Group ◽  
High Risk Group ◽  
Risk Classification ◽  
Recurrence Rates ◽  
Disease Specific

ObjectiveTo compare the performance of the new ESGO-ESTRO-ESP (European Society of Gynecological Oncology-European Society for Radiotherapy & Oncology-European Society for Pathology) 2020 risk classification system with the previous 2016 risk classification in predicting survival and patterns of recurrence in the Danish endometrial cancer population.MethodsThis Danish national cohort study included 4516 patients with endometrial cancer treated between 2005 and 2012. Five-year Kaplan–Meier adjusted and unadjusted survival estimates and actuarial recurrence rates were calculated for the previous and the new classification systems.ResultsIn the 2020 risk classification system, 81.0% of patients were allocated to low, intermediate, or high-intermediate risk compared with 69.1% in the 2016 risk classification system, mainly due to reclassification of 44.5% of patients previously classified as high risk to either intermediate or especially high-intermediate risk. The survival of the 2020 high-risk group was significantly lower, and the recurrence rate, especially the non-local recurrence rate, was significantly higher than in the 2016 high risk group (2020/2016, overall survival 59%/66%; disease specific 69%/76%; recurrence 40.5%/32.3%, non-local 34.5%/25.8%). Survival and recurrence rates in the other risk groups and the decline in overall and disease-specific survival rates from the low risk to the higher risk groups were similar in patients classified according to the 2016 and 2020 systems.ConclusionThe new ESGO-ESTRO-ESP 2020 risk classification system allocated fewer patients to the high risk group than the previous risk classification system. The main differences were lower overall and disease-specific survival and a higher recurrence rate in the 2020 high risk group. The introduction of the new 2020 risk classification will potentially result in fewer patients at high risk and allocation to the new high risk group will predict lower survival, potentially allowing more specific selection for postoperative adjuvant therapy.

scholarly journals Statistical Framework in Support of a Revised Children's Oncology Group Neuroblastoma Risk Classification System

2018 ◽  
pp. 1-15 ◽  
Author(s):  
Arlene Naranjo ◽  
Meredith S. Irwin ◽  
Michael D. Hogarty ◽  
Susan L. Cohn ◽  
Julie R. Park ◽  
...  
Keyword(s):  
Classification System ◽  
Recursive Partitioning ◽  
Diagnostic Category ◽  
Staging System ◽  
Risk Classification ◽  
Imaging Data ◽  
Oncology Group ◽  
Clinical Validity ◽  
International Consensus ◽  
Str Analysis

Purpose The International Neuroblastoma Risk Group (INRG) Staging System (INRGSS) was developed through international consensus to provide a presurgical staging system that uses clinical and imaging data at diagnosis. A revised Children's Oncology Group (COG) neuroblastoma (NB) risk classification system is needed to incorporate the INRGSS and within the context of modern therapy. Herein, we provide statistical support for the clinical validity of a revised COG risk classification system. Patients and Methods Nine factors were tested for potential statistical and clinical significance in 4,569 patients diagnosed with NB who were enrolled in the COG biology/banking study ANBL00B1 (2006-2016). Recursive partitioning was performed to create a survival-tree regression (STR) analysis of event-free survival (EFS), generating a split by selecting the strongest prognostic factor among those that were statistically significant. The least absolute shrinkage and selection operator (LASSO) was applied to obtain the most parsimonious model for EFS. COG patients were risk classified using STR, LASSO, and per the 2009 INRG classification (generated using an STR analysis of INRG data). Results were descriptively compared among the three classification approaches. Results The 3-year EFS and overall survival (± SE) were 72.9% ± 0.9% and 84.5% ± 0.7%, respectively (N = 4,569). In each approach, the most statistically and clinically significant factors were diagnostic category (eg, NB, ganglioneuroblastoma), INRGSS, MYCN status, International Neuroblastoma Pathology Classification, ploidy, and 1p/11q status. The results of the STR analysis were more concordant with those of the INRG classification system than with LASSO, although both methods showed moderate agreement with the INRG system. Conclusion These analyses provide a framework to develop a new COG risk classification incorporating the INRGSS. There is statistical evidence to support the clinical validity of each of the three classifications: STR, LASSO, and INRG.

The TAME Risk Classification System: Acute Toxicity Burden and IPD Analysis of RTOG 90–03

2005 ◽  
Vol 63 ◽  
pp. S131 ◽  
Author(s):  
T.F. Pajak ◽  
A. Trotti ◽  
C.K. Gwede ◽  
R. Paulus ◽  
J. Cooper ◽  
...  
Keyword(s):  
Acute Toxicity ◽  
Classification System ◽  
Risk Classification
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