Simulation methods results—Example 1.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-partners-with-ehs-for-digital-advertising-representation-20210416",title:"IntechOpen Partners with EHS for Digital Advertising Representation"},{slug:"intechopen-signs-new-contract-with-cepiec-china-for-distribution-of-open-access-books-20210319",title:"IntechOpen Signs New Contract with CEPIEC, China for Distribution of Open Access Books"},{slug:"150-million-downloads-and-counting-20210316",title:"150 Million Downloads and Counting"},{slug:"intechopen-secures-indefinite-content-preservation-with-clockss-20210309",title:"IntechOpen Secures Indefinite Content Preservation with CLOCKSS"},{slug:"intechopen-expands-to-all-global-amazon-channels-with-full-catalog-of-books-20210308",title:"IntechOpen Expands to All Global Amazon Channels with Full Catalog of Books"},{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"}]},book:{item:{type:"book",id:"3749",leadTitle:null,fullTitle:"Evolutionary Computation",title:"Evolutionary Computation",subtitle:null,reviewType:"peer-reviewed",abstract:"This book presents several recent advances on Evolutionary Computation, specially evolution-based\r\noptimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. \r\n\r\nThis book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. \r\n\r\nThe intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field.",isbn:null,printIsbn:"978-953-307-008-7",pdfIsbn:"978-953-51-5913-1",doi:"10.5772/234",price:159,priceEur:175,priceUsd:205,slug:"evolutionary-computation",numberOfPages:584,isOpenForSubmission:!1,isInWos:1,hash:"413cf10893f15955d121a4934493da16",bookSignature:"Wellington Pinheiro dos Santos",publishedDate:"October 1st 2009",coverURL:"https://cdn.intechopen.com/books/images_new/3749.jpg",numberOfDownloads:63720,numberOfWosCitations:80,numberOfCrossrefCitations:61,numberOfDimensionsCitations:115,hasAltmetrics:0,numberOfTotalCitations:256,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 16th 2013",dateEndSecondStepPublish:"June 6th 2013",dateEndThirdStepPublish:"September 10th 2013",dateEndFourthStepPublish:"December 9th 2013",dateEndFifthStepPublish:"January 8th 2014",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,editors:[{id:"125844",title:"Prof.",name:"Wellington",middleName:"Pinheiro Dos",surname:"Santos",slug:"wellington-santos",fullName:"Wellington Santos",profilePictureURL:"https://mts.intechopen.com/storage/users/125844/images/4878_n.jpg",biography:"Wellington Pinheiro dos Santos holds a degree in Electrical and Electronics Engineering (2001) and MS in Electrical Engineering (2003) from the Federal University of Pernambuco, and Ph.D. in Electrical Engineering from the Federal University of Campina Grande (2009). 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Tiefenbacher and Davod Poreh",coverURL:"https://cdn.intechopen.com/books/images_new/9846.jpg",editedByType:"Edited by",editors:[{id:"73876",title:"Dr.",name:"John P.",surname:"Tiefenbacher",slug:"john-p.-tiefenbacher",fullName:"John P. Tiefenbacher"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9246",title:"Satellites Missions and Technologies for Geosciences",subtitle:null,isOpenForSubmission:!1,hash:"f23d04613b089dae40f81342c3e7c7f4",slug:"satellites-missions-and-technologies-for-geosciences",bookSignature:"Vladislav Demyanov and Jonathan Becedas",coverURL:"https://cdn.intechopen.com/books/images_new/9246.jpg",editedByType:"Edited by",editors:[{id:"154597",title:"Prof.",name:"Vladislav",surname:"Demyanov",slug:"vladislav-demyanov",fullName:"Vladislav Demyanov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"68245",title:"Probabilistic Modeling of Failure",doi:"10.5772/intechopen.83461",slug:"probabilistic-modeling-of-failure",body:'\nTraditionally, failure analysis is conducted using deterministic techniques to assess the operability and integrity of industrial systems. These techniques lack the ability to report or predict the probabilistic nature of the systems’ behavior. Moreover, they ignore the probabilistic and random nature of the external factors that have direct impact on the performance of the systems. Implementing these techniques may produce inadequate assessment and eventually, may lead to wrong decisions concerning the integrity and reliability of the evaluated systems. To make an informed and reliable decision about the reliability and operability of such systems, probabilistic failure analysis should be adopted as an alternative analysis technique. This technique should be made as an integral part of the decision process as well as be a part of the overall organization’s risk control.
\nThis chapter presents techniques that assist in the analysis of failure using engineering probabilistic methods. They include simulation as well as analytical methods. Simulation methods can be conducted using Monte Carlo simulation technique. Two different Monte Carlo simulation approaches are presented in this chapter. These are, the counting approach and sample statistics approach. The main drawback with simulation is that it takes great deal of time to perform and may require an extensive processing power. However, it is an essential step in the analysis to validate the results obtained by the analytical methods.
\nSome of the analytical methods include first order reliability method (FORM) and second order reliability method (SORM). FORM involves two approaches to calculate probability of failure, these are first order and second moment (FOSM) and advanced FOSM.
\nThe focus of this chapter will be only on the FORM with the assumption that all random variables are uncorrelated. Analysis that require the use of FORM for correlated random variables is beyond the scope of this chapter. Likewise, analysis requiring the application of SORM to analyze limit state functions involving second order representation is beyond the scope of this chapter.
\nFailure can be defined as the inability of industrial systems or subsystems either partially or totally to satisfy operational requirements as set forth by design specifications. Failure of a system could be partial or complete; in either case the consequences of failure may result in adverse consequences. Interruption of services, degraded performance, system shutdown, environmental damage and customers’ dissatisfaction are some of the consequences. Such consequences may lead to financial losses, liabilities and destroyed image of the operating company. As an example, if failure involves leak detection system to detect oil and gas leakage from subsea oil and gas pipelines, consequences could be so severe. Pollution of the ocean, damage to the fishery and tourism industries are some of the major consequences.
\nThe system fails when the imposed demand or load on the system exceeds its capacity or resistance. The strength or the capacity of the system is a design parameter that specifies the maximum load the system can endure or the maximum demand the system can satisfy. The variabilities of the system’s capacity to satisfy the demand or load imposed on it are mainly attributed to the inherent uncertainties of the operation characteristics of the system’s components as well as external environmental factors. Therefore, the capacity of the system is assumed to be probabilistic in nature that varies from time to time due to the reasons mentioned above. Likewise, using same argument the load or demand imposed on the system are considered probabilistic in nature due to the effect of the varying environmental conditions.
\nConsidering the above, the performance function of the system or sometime is called limit state function can be formulated as the difference between the system’s capacity and the load or demand imposed on it. The same argument can be used for production facility, the performance is the difference between supply and demand, supply being the capacity, or the strength and demand is the load. If the two parameters are the same then it can be said that the system is at a limit state, if the system cannot meet the demand then the system is at a failure state, and if the system capacity exceeds the load imposed on it, the system is at a satisfactory state.
\nKnowing in advance when the system is going to fail or degrade in performance is an essential step in the failure analysis. Under this step, the probability of failure is calculated in terms of the random variables affecting the performance of the system. There are several approaches found in the literature that can be used to evaluate the probability of failure either analytically or by simulation. Analytical methods approximate the probability of failure by using first order reliability method (FORM) or second order reliability method (SORM). The FORM uses two approximation techniques that evaluates the probability of failure, these are the first order and second moment (FOSM) and advanced first order second moment (AFOSM) techniques.
\nCalculating the probability of failure based on the methods mentioned above can be used to predict the ability of the system to satisfy operational as well as safety requirements during its life cycle. Combining this analysis with risk analysis, the consequences of failure can be easily determined. First order reliability method consists of two techniques namely:
\nFOSM makes use of second moment statistics (mean and variance) and ignores higher moments (skewness and kurtosis) of the random variables. It evaluates the performance function by using the first order Taylor series expansion of the limit state function (LSF) at the mean value. This method is used when the performance function is linear having statistically independent, normally distributed and noncorrelated random variables
Performance function can be defined as [3, 5, 10]:
\nwhere C is the capacity and D is the demand are statistically independent random variables and are assumed to be normally distributed. Failure occurs when:
\nThen the probability of failure (
Or
\nFigure 1 shows the probability density function (
Probability density function of the performance function.
The probability of failure is expressed as [3, 5, 10]:
\nAlternatively, the performance function
The integration of the performance function as indicated in Eq. (5) is performed for the region where
Then the mean and variance are given as:
\nThe reliability index (
The reliability index is computed for every failure mode, where the probability of failure is expressed as:
\nThis method is simple to use and assumes that the random variables are normally distributed. All is needed for the calculation is the knowledge of the mean and the standard deviation and it is not necessary to know the distribution of the random variables. The downside of this method is that it can cause error in the final results if the function is nonlinear or if the tail of the distribution cannot be approximated by normal distribution. Moreover, if the function is nonlinear it will be provided different answer than that if it is linear. Advanced FOSM is used to deal with the limitations of the FOSM mentioned above.
\nAFOSM provides solution for linear and nonlinear performance function by determining the shortest distance from the origin to the failure surface. This method is also called Hasofer-Lind method. It evaluates the probability of failure for the limit state function or the performance function by determining the most probable failure point instead of the mean. Hasofer and Lind developed this advanced method in 1974 which is called Hasofer-Lind method and is abbreviated as H-L method. As stated above, the main objective of this method is to estimate the failure point which is the shortest distance from the origin to the failure surface that separates the failure region from the safe region. This can be clearly shown in Figure 2. The failure point is sometimes called in the literature design point or check point, but in this chapter, it will be referred to as the most probable point of failure (MPPF). Let us consider a limit state function/performance function with normally distributed and independent random variables
(a) Nonlinear limit state function—original coordinates. (b) Nonlinear limit state function—transformed coordinates.
This method transforms the random variables into reduced form as:
\nThe performance function is then formulated in terms of the reduced random variables as:
\nFigure 2 shows the plot of the limit state function in the original as well as the transformed coordinates. It shows that the MPPF is the tangent point on the curve
To find the MPPF
Using chain rule for derivative and considering that relationship between
and using Eqs. (14) and (15) the partial derivative
Substituting Eq. (19) into Eq. (17) gives:
\nThe mean of
The variance is expressed as:
\nIt must be noted that constants have no variance, their variances equal to zero. The first term of the Taylor expansion in Eq. (27) is constant; therefore, its variance equals to zero. Similarly, the variance at the mean value is zero.
\nAlternatively, Eq. (31) can be calculated as [6]:
\nThe reliability index is calculated as:
\nThe directional cosine
It can be shown from Figure 2 that:
\nUsing Eqs. (14), (15) and (36) we can determine the design point in the original coordinates as:
\nThe probability of failure,
The steps mentioned above are used with the assumption that the random variables are normally distributed. For non-normally distributed variables additional steps are needed to determine the mean and standard deviation of the equivalent normal distribution as listed below. These steps should be carried out after step number two to determine mean and standard deviation of the equivalent normal distribution. Assuming the random variables are statistically independent and non-normally distributed:
Determine the distribution parameters.
Compute the cumulative distribution function
Compute the values of the standard deviation,
It must be noted that
Compute the standard normal variable (
For a log normally distributed random variable, distribution parameters
The pdf and
For other distribution types the readers are referred to Refs. [4, 7, 8].
\nAlternatively, the probability of failure is computed using Monte Carlo simulation method. Two methods are considered in this chapter, the counting and sample statistics methods. The simulation is conducted using computer programs such MATLAB, C++ or MINITAB or any other simulation programming packages.
\nThe counting method is formulated by dividing the number of simulation cycles at the events when the
i.
ii.
The performance function for a system has been formulated as:
\n
Determine the probability of failure using simulation methods: Monte Carlo (MCS) sample statistics and Monte Carlo counting simulation methods.
Determine the probability of failure using the analytical methods: FOSM and AFOSM (Hasofer-Lind) methods.
Monte Carlo simulation was conducted for both methods, the sample statistics and counting methods. The number of simulation cycles used in the analysis are 2e5 and 1e6 cycles (Table 1).
See Table 2.
Simulation methods results—Example 1.
The performance function for a leak detection system has been formulated as [2]:
\nDetermine the probability of failure using Monte Carlo counting simulation method.
Determine the probability of failure using AFOSM method.
This example is adopted from reference [1, 2], with some modifications.
\nMonte Carlo simulation was conducted for both methods, the sample statistics and counting methods as indicated in the table.
\nTable 3 indicates that the probability of failure converges to
Simulation methods results—Example 2.
First the
The partial derivatives:
\nThe probability of failure obtained by counting method is very close to that obtained by AFOSM (Table 4).
\nSummary of the analytical methods results—Example 2.
A pipeline segment is suffering corrosion that grows annually at steady rate. The extent of the initial growth has been estimated to be 4.7 mm and it is assumed that it follows log normal distribution with standard deviation of 1.1.
\nThe corrosion annual growth follows log normal distribution with a mean and standard deviation values of 0.2 and 0.01. The pipeline wall thickness follows normal distribution with a mean and standard deviation values of 14 mm and 4.7 respectively. The critical pipeline wall thickness has been determined to be 80% of the wall thickness [3]. A summary of the relevant information pertaining to the pipeline corrosion is presented in the net table, Table 5.
\nPipeline corrosion data—Example 3.
The owner of the pipeline decides not to repair the corrosion and wants to know if the pipeline can survive for the next 14 years without causing a leakage. It has been decided that in order to be in the safe side the maximum acceptable probability of failure has been set to 1e4 [3].
\nSolve this problem using analytical method as well as Monte Carlo (MCS) simulation method.
\nMonte Carlo simulation produced the following results as shown in Table 6. The probability of failure and beta converge to the following values:
\nSimulation methods results—Example 3.
Here only the counting method is used because the sampling method produces different results. The sampling method produces accurate results for linear and normal limit state/performance function only.
\nAssume the initial value for each random variable to be its mean.
\nFor non-normal variables, the standard deviation and mean values of the equivalent normal variables are calculated using Eqs. (40) and (41).
\nCompute the pdf of the original non-normal variable (log normal distribution) using Eq. (45):
\nCompute the
Compute the mean and standard deviation of the equivalent normal variable at the design point using Eqs. (40) and (41):
\nThe same procedures outline for initial corrosion extent,
Mean and standard deviation for normal variable
Mean and standard deviation for the equivalent normal distribution for the non-normal variables,
Analytical methods results—Example 3.
Table 8 shows that the obtained values for beta and the probability of failure are so close to each other.
\nComparison of results—Example 3.
The calculated probability of failure exceeds the target probability of failure
Two systems, system A and system B, each system has three main components and each component with it; the probability distribution type and its parameters are shown in Table 9. It is required to determine the probability of failure of each component using FOSM, AFOSM and Monte Carlo simulation [9].
\nCapacity and demand variables—Example 4.
Formulate a general LSF for each component see the (Table 10).
\nSimulation methods results systems A and B—Example 4.
Analytical solution see (Table 11).
\nAnalytical method results—Example 4.
Prosthetic joint infection (PJI) affects about 1–3% of patients undergoing total joint arthroplasty [1]. In some units the infection rate is reported to be as high as 5% [2]. It is one of the most devastating complications and poses significant challenges for the patient, health care providers and the treating institution. The financial cost of treating a single case of PJI can be as high as £100,000 [2]. Costs for patients are even higher, with long hospital stay, multiple operations, associated pain and suffering, reduced life quality as well as risks associated with surgical morbidity and mortality. Diagnosis and management of PJI remains controversial and complex. There is no universal definition of the PJI. The definition of PJI proposed by the International Consensus Meeting on Periprosthetic Joint Infection is the most universally accepted one [3]. Other definitions also exist. George et al. [4] acknowledged 7 definitions produced by various consensus meetings. This illustrates that PJI remains a debatable and controversial topic and diagnosis is not straight forward. There is no one single test that can adequately diagnose PJI. Up to 10% of cases undergoing revision for aseptic loosening are later found to have prosthetic joint infection [5]. PJI can present in variety of ways and at varying phases from the time primary arthroplasty implantation. Tsukayama et al. proposed a classification system that divided PJI into four categories [6].
It can be challenging for an individual surgeon to make an accurate diagnosis when faced with a patient with a painful arthroplasty. One way of addressing this has been to manage this complex group of patients with a multidisciplinary team. Failure to make a timely and accurate diagnosis can significantly compromise therapeutic options and have a negative impact on the result of surgical treatment [7]. Furthermore, if PJI is not recognised, it may lead to systemic symptoms such as bacteraemia and septicaemia.
PJI can be challenging to treat, and patients may need a number of major surgical procedures, coupled with antimicrobial treatment for several weeks to eradicate the infection [8]. Treatment of PJI of the knee may be associated with a long period of disability with possible immobilisation of the knee. This may lead to a poor functional outcome. Recurrence of the infection is high and reported between 8% and 70% [9] and complications associated with surgery are common. Furthermore, PJI is associated with significant mortality. Berend et al. [10] reported that 11% patients treated for PJI with a 2 stage regime died between the first and second stages of surgical treatment. Zmistowski et al. [3] found that the 5-year survivorship of patients with PJI is worse than for some common cancers including breast cancer or testicular cancer. For this reason PJI must be managed expeditiously, providing patients with all available expertise to achieve the optimum outcome. Added to this is the psychological burden associated with the issues described and its impact on post operative function [11]. This combination of the knowledge that there has been a complication or suboptimal outcome, multiple surgical procedures, prolonged hospital stay, prolonged disability and associated medical comorbidities as well as social isolation and pain illustrates multiple issues which can be associated with patients presenting with PJI and the multiple facets which require management in a synchronised manner. These factors have been acknowledged in other aspects of orthopaedic surgery and it is acknowledged that optimal outcomes result from a multidisciplinary approach to management [12, 13].
The physical, but also psychological needs of patients should be addressed. PJI may be emotionally difficult to cope with and lead to sequalae such as depression and anxiety [19]. Many patients struggle with the impact that the treatment of PJI has on their personal and family lives’. Patients’ depression may require treatment and support during the treatment as well as during the recovery phases [20].
Ideally personnel should be present in the same location in order to provide a seamless, clinically and cost-efficient service to patients with PJI. They should be involved in all stages of the management pathways including, diagnosis, treatment (both surgical and non-surgical) and long term follow up. The multidisciplinary approach has made a significant difference in care of oncology patients. Time to diagnosis and clinical outcomes have all been shown to improve when the MDT functions well well [21, 22]. There is no published evidence to the authors’ knowledge on the management of PJI with this approach however the principles of diagnosis and factors influencing management and outcomes of patients with PJI are similar. It seems intuitive therefore that a similar approach to treatment might produce similar outcomes.
Most published studies examining the benefits of MDT’s have focused on clinical results [22]. There is a relative paucity of data on the components of the MDT. An important principle of care delivery in this setting is consideration of the wholistic needs of the patient and including appropriate specialists to address these issues. In the context of PJI the following team members are required:
At the authors’ institution, there is an established referral network for complex cases including those presenting with PJI. Clinicians from the region can refer any patient who needs complex arthroplasty assessment and treatment, including those with PJI to a centralised hub. There is a standardised referral proforma and MDT coordinator who promptly responds to all referrals. There is a weekly MDT attended by complex arthroplasty surgeons, a CNS and radiologists with an interest in musculoskeletal medicine. Cases are discussed and either advice is provided or a decision on transfer of the patient to the Hub Hospital is organised. In complex cases when surgery is required, surgical planning is performed and details such as surgical approach, instruments and required implants are all discussed. Each week between 10 to 20 cases are discussed. Advice of plastic surgeons, vascular surgeons and microbiologists is available on request. There is also a monthly MDT meeting attended by the same team of complex arthroplasty surgeons as well as microbiologists with an interest in bone and joint infection and outpatient antibiotic treatment (OPAT) team. All cases undergoing treatment for infection are discussed, plans for surgical and non-surgical treatment are established and progress of treated patients is discussed. This ensures that most appropriate treatment plan is made for each individual.
The East Midlands Specialist Orthopaedic Network (EMSON) (Nottingham, UK) was established and its success has been reported [32]. All referrals are received by email by the MDT coordinator. The meetings are conducted using secure videolink, with complex arthroplasty surgeons from Nottingham University Hospital and microbiologists attending while consultants from neighbouring hospitals dial in to discuss challenging cases. During first 6 months 166 cases were discussed, 43% of which the initial plan was amended as the result of the discussion. In several cases, there was a significant alteration to the treatment plan. Referring surgeons are also encouraged to come to tertiary centre with the potential for joint consultant operating. This improves the experience of all clinicians involved.
The potential benefits of care delivered via a MDT approach can be experienced on a variety of levels:
The treatment of PJI is labour and resource intensive. Patients often stay on the ward for extended periods and face a higher risk of surgical and medical complications that non-infected cases. It is likely that the number of referrals and number of treated patients will increase over time which increases this burden [32].
Vaneghan et al. have shown that the cost of surgical treatment is significantly higher than septic revisions [33]. There is a potential risk of rapid depletion of financial resources. Renumeration strategies need to be established prior to starting this type of service [18, 33].
The logistics and practical aspects of establishing a MDT requires careful attention to detail. An understanding of what is required on a practical level is important. Meetings of large numbers of specialists takes these services from other departments. To the authors’ knowledge there has been no definition of the optimal constituents a MDT or of the minimal number of specialists required or whether the teams involved in diagnostic and therapeutic parts of the patient journey should be different.
Job planning for all the members of the team should be coordinated to allow all members to meet or dial in to discuss cases. Surgeons, radiologists, microbiologists and other health care professionals involved in PJI management need to find time during their busy weekly schedule for MDT to work. Furthermore, when transfer of the patient is necessary to the specialist centre, logistical arrangements need to be in place to avoid delays.
Another unexplored aspect of delivering care in this way is the issue of responsibility and autonomy. The MDT moves away from the heirarchical system in which decisions are made by one senior individual towards one where there is shared decision making. This raises the subject of accountability. When a decision is taken by a group, who is responsible and who, if anyone, is accountable when things go wrong? For the same reason there can be a perceived risk to the autonomy of the referring surgeon. These issues have not been addressed.
In conclusion the management of PJI is complex and multifactorial. Multidisciplinary management has resulted in improved clinical results in similar settings setting such as tumour surgery however establishment of multidisciplinary care presents significant challenges to the treating institution.
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