Our strategic priority was to create a common point of reference for operational risk taxonomies, laying the foundations which allow industry debate and consistent industry sharing of insights and data going forward.\"Simon Wills, Executive Director, ORX
The ORX Reference Taxonomy for operational and non-financial risks is made up of the Event Type Taxonomy and the Cause and Impact Taxonomy. The Event Type Taxonomy covers level 1 and level 2 risks, including more 'contemporary' risks, such as conduct, cyber and third party. The Cause and Impact Taxonomy complements the Event Type Taxonomy and covers causes and impacts to provide even more insight.
Part of our mission at ORX is to support the management and measurement of operational risk beyond our membership. So, the ORX Reference Taxonomy, consisting of both the Event Type and the Cause and Impact Taxonomies, is available for you to download and start using at your firm for free.
To help the operational risk community get the most from the ORX Reference Taxonomy, we've created guidance that is available to purchase alongside it. The guidance gives you detail on each level 1 risk, explains how to use flags and risk themes to add further context and insights to your data, provides information and definitions for specific cause categories and more.
The management of women with cervical intraepithelial neoplasia (CIN) is crucial, given that improper management may increase the risk of developing cervical cancer, whereas overtreatment increases the risk of complications related to preterm delivery or other. Therefore, appropriate management is essential in terms of cancer prevention [7, 8].
Some HPV infections may persist, despite the relatively high post-conization HPV clearance rate . A number of risk factors for residual or recurrent disease have been identified in the treatment of CIN lesions. According to Nam and Heymans, these are: age, menopause status, cytology grade, margin involvement and HPV viral load [10, 11].
In Romania cervical cancer is the first leading cause of cancer deaths in women aged 15 to 44 years, with over 4300 new cases diagnosed each year. Infection with HR HPV types was found in 86.8 % of the cases and the prevalence HPV type 16 is 45 % among women with high grade lesions . A national program that started in 2012 for the screening and prevention of cervical cancer is still ongoing in Romania. In addition, all women aged over 16 can benefit from a free Pap smear. Moreover, the cases with cytological abnormalities are referred to specialized centers.
Statistical analysis was conducted using the following software SPSS v17, Epi Info 7 and Microsoft Excel. For this data we computed a descriptive statistics, we used parametrical statistical tests (Z test for proportion) and we performed a risk analysis using a chi square test and risk indicators. The cutoff point was considered the median age in our group. The Z test for proportion was computed in order to measure the persistence rate of HPV types. We performed a risk analysis considering as an exposure the age above the median age in our group.
Despite the removal of the entire lesion by cone excision, with negative margins, the HPV infection can persist in some cases. Studies investigating the clearance/persistence of HPV infection after LEEP have reported that age, lesion grade, and margin status are risk factors for HPV persistence.
We consider this information valuable, as HPV type 16 seems to have the highest pathogenicity. We did not find in literature data about age as a risk factor for the persistence of HPV type 16 alone. For that reason, we consider that this adds value to our study.
In our study group we identified a high percentage (68 %) of co-infection with multiple HPV types. According to the findings of Jaisamrarn et al , concomitant HPV infection increase the risk of progression to a lesion, suggesting that multiple HPV infections could influence disease progression. We consider that our high rate of patients co-infected with multiple HPV types is due to the selection of patients with HSIL only .
The American Society of Anesthesiologists (ASA) physical status classification system came about to offer perioperative clinicians a simple categorization of a patient's physiological status that can help predict operative risk. The ASAPS originated in 1941 and has seen some revisions since that time. This activity covers the American Society of Anesthesiologists (ASA) physical status classification system's classification of patients so healthcare teams can make informed decisions as to some meaningful correlated outcomes. This activity describes the current understanding regarding the application or misapplication of the ASAPS. This activity illustrates the relevant considerations and controversies regarding the ASAPS. This activity reviews the evaluation and application of the ASAPS and highlights the role of the interprofessional team in evaluating and treating patients while utilizing this classification system.
Objectives:Identify the included factors and their limitations that influence the American Society of Anesthesiologists (ASA) physical status classification system.Review the issues of concern and controversy surrounding the American Society of Anesthesiologists (ASA) physical status classification system.Summarize each category within the American Society of Anesthesiologists (ASA) physical status classification system.Outline and explain interprofessional team strategies for improving care coordination and communication using the American Society of Anesthesiologists (ASA) physical status classification system to facilitate the management of patients in the perioperative period.Access free multiple choice questions on this topic.
The American Society of Anesthesiologists (ASA) physical status classification system came about to offer perioperative clinicians a simple categorization of a patient's physiological status to help predict operative risk. The ASAPS originated in 1941 and has seen some revisions since that time. Unfortunately, while the ordinal classification scheme is simple, it is far from an ideal preoperative measure for assessment. Once properly reviewed, the differences that might separate any given patient from being classified in any ASAPS classification category from another patient in either a higher or lower category may be extreme from one healthcare provider, group, or system compared to another. While its utility as a simple classification is perhaps its best feature, this also portends its serious deficiencies. There is certainly a considerable body of evidence correlating ASAPS classification with a variety of useful outcomes.
As it was neither developed nor intended to be used to predict risk with anesthesia or surgery, it is difficult to utilize it in the individual management of any patient beyond very general concepts. More concerning are the great number of areas, purposes, healthcare providers, and guidelines/standards that attempt to utilize the ASAPS for an increasing plethora of purposes for which it was never intended, which will only invariably lead to a host of unintended and potentially negative consequences. This is punctuated even more so when one considers the tremendous variability and inconsistency in the classification of any one patient among non-anesthesiologists as well as even among anesthesiologists, not to mention the variability and inconsistency between anesthesiologists versus non-anesthesiologists.
Perioperative staff could utilize this classification system for perioperative outcomes management. However, this must be considered in the larger context of the variability and inconsistency with classification between classifying entities, as meta-analyses have shown the ASAPS having a sensitivity around 0.74 (95% confidence intervals 0.73 to 0.74), specificity around 0.67 (0.67 to 0.67), and a receiver operating curve area under the summary of around 0.736 (0.725 to 0.747) in predicting mortality postoperatively. For robust and reliable preoperative risk assessment, one needs to consider a variety of additional factors discretely in addition to the information provided by a composite index classification:
Companies should tailor their risk management processes to these different risk categories. A rules-based approach is effective for managing preventable risks, whereas strategy risks require a fundamentally different approach based on open and explicit risk discussions. To anticipate and mitigate the impact of major external risks, companies can call on tools such as war-gaming and scenario analysis.
In this article, we present a new categorization of risk that allows executives to tell which risks can be managed through a rules-based model and which require alternative approaches. We examine the individual and organizational challenges inherent in generating open, constructive discussions about managing the risks related to strategic choices and argue that companies need to anchor these discussions in their strategy formulation and implementation processes. We conclude by looking at how organizations can identify and prepare for nonpreventable risks that arise externally to their strategy and operations.
A company voluntarily accepts some risk in order to generate superior returns from its strategy. A bank assumes credit risk, for example, when it lends money; many companies take on risks through their research and development activities.
Strategy risks are quite different from preventable risks because they are not inherently undesirable. A strategy with high expected returns generally requires the company to take on significant risks, and managing those risks is a key driver in capturing the potential gains. BP accepted the high risks of drilling several miles below the surface of the Gulf of Mexico because of the high value of the oil and gas it hoped to ex