Consensus Method Research Articles

Examining the Prevalence of Iron-deficiency Anemia in Children with Cyanotic Congenital Heart Disease Admitted to the Pediatric Heart Department of Imam Reza Hospital in Mashhad between 2011 and 2019: A Cross-sectional Observational Study

Introduction: This study was performed to investigate the frequency of iron-deficiency anemia and some related factors in children with cyanotic congenital heart disease between 2011 and 2019 in Mashhad City, Iran. Method: This cross-sectional observational study was conducted with a consensus method on children under 14 years of age. The samples were diagnosed with cyanotic congenital heart disease in the pediatric heart department of Imam Reza Hospital in Mashhad and had an oxygen saturation of less than 94%. The information from the patients' files was recorded in the questionnaire. The studied children were divided into five age groups to evaluate iron-deficiency anemia. The mean and lower limit of normal erythrocyte indices were determined, and the patients with anemia were identified based on mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), and red blood cell distribution width (RDW). The data were statistically analyzed in SPSS23 software using Chi-square and Mann-Whitney statistical tests. A significance level of 0.05 was considered. Results: Among 250 examined patients, 90 cases were included in the study (54 boys and 36 girls). Angiography was the reason for half of these patients' referral to the hospital. The highest frequency was related to patients with tetralogy of Fallot (40%). The frequency rates of iron-deficiency anemia based on MCV and MCH erythrocyte indices were 10% and 17.8%, respectively. Moreover, 64.4% of patients had increased RDW levels. Cyanosis attacks were reported positive in 85.4% of the studied population. Of note, the frequency of cyanosis attacks was not significantly different in the two gender groups (P<0.494). Conclusion: Considering the high prevalence of iron-deficiency anemia, especially in children with congenital heart disease, the treatment of this disorder is necessary for all such children and should not be neglected

Maintenance of genetic diversity in subdivided populations using genomic coancestry matrices.

For both undivided and subdivided populations, the consensus method to maintain genetic diversity is the Optimal Contribution (OC) method. For subdivided populations, this method determines the optimal contribution of each candidate to each subpopulation to maximize global genetic diversity (which implicitly optimizes migration between subpopulations) while balancing the relative levels of coancestry between and within subpopulations. Inbreeding can be controlled by increasing the weight given to within-subpopulation coancestry (λ). Here we extend the original OC method for subdivided populations that used pedigree-based coancestry matrices, to the use of more accurate genomic matrices. Global levels of genetic diversity, measured as expected heterozygosity and allelic diversity, their distributions within and between subpopulations, and the migration pattern between subpopulations, were evaluated via stochastic simulations. The temporal trajectory of allele frequencies was also investigated. The genomic matrices investigated were (i) the matrix based on deviations of the observed number of alleles shared by two individuals from the expected number under Hardy-Weinberg equilibrium; and (ii) a matrix based on a genomic relationship matrix. The matrix based on deviations led to higher global and within-subpopulation expected heterozygosities, lower inbreeding and similar allelic diversity than the second genomic and pedigree-based matrices when a relatively high weight was given to the within-subpopulation coancestries (λ ≥ 5). Under this scenario, allele frequencies moved only slightly away from the initial frequencies. Therefore, the recommended strategy is to use the former matrix in the OC methodology giving a high weight to the within-subpopulation coancestry.

A Framework for Data Privacy Preserving in Supply Chain Management Using Hybrid Meta-Heuristic Algorithm with Ethereum Blockchain Technology

Blockchain is a recently developed advanced technology. It has been assisted by a lot of interest in a decentralized and distributed public ledger system integrated as a peer-to-peer network. A tamper-proof digital framework is created for sharing and storing data, where the linked block structure is utilized to verify and store the data. A trusted consensus method has been adopted to synchronize the changes in the original data. However, it is challenging for Ethereum to maintain security at all blockchain levels. As such, “public–private key cryptography” can be utilized to provide privacy over Ethereum networks. Several privacy issues make it difficult to use blockchain approaches over various applications. Another issue is that the existing blockchain systems operate poorly over large-scale data. Owing to these issues, a novel blockchain framework in the Ethereum network with soft computing is proposed. The major intent of the proposed technology is to preserve the data for transmission purposes. This new model is enhanced with the help of a new hybrid algorithm: Adaptive Border Collie Rain Optimization Algorithm (ABC-ROA). This hybrid algorithm generates the optimal key for data restoration and sanitization. Optimal key generation is followed by deriving the multi objective constraints. Here, some of the noteworthy objectives, such as information preservation (IP) rate, degree of modification (DM), false rule (FR) generation, and hiding failure (HF) rate are considered. Finally, the proposed method is successfully implemented, and its results are validated through various measures. The recommended module ensures a higher security level for data sharing.

Does consensus contours improve robustness and accuracy on ^{18}F-FDG PET imaging tumor delineation?

Purpose:The aim of this study is to explore the robustness and accuracy of consensus contours with 225 nasopharyngeal carcinoma (NPC) clinical cases and 13 extended cardio-torso simulated lung tumors (XCAT) based on 2-deoxy-2-[^{18}F]fluoro-D-glucose (^{18}F-FDG) PET imaging.Methods:Primary tumor segmentation was performed with two different initial masks on 225 NPC ^{18}F-FDG PET datasets and 13 XCAT simulations using methods of automatic segmentation with active contour, affinity propagation (AP), contrast-oriented thresholding (ST), and 41% maximum tumor value (41MAX), respectively. Consensus contours (ConSeg) were subsequently generated based on the majority vote rule. The metabolically active tumor volume (MATV), relative volume error (RE), Dice similarity coefficient (DSC) and their respective test–retest (TRT) metrics between different masks were adopted to analyze the results quantitatively. The nonparametric Friedman and post hoc Wilcoxon tests with Bonferroni adjustment for multiple comparisons were performed with P< 0.05 considered to be significant.Results:AP presented the highest variability for MATV in different masks, and ConSeg presented much better TRT performances in MATV compared with AP, and slightly poorer TRT in MATV compared with ST or 41MAXin most cases. Similar trends were also found in RE and DSC with the simulated data. The average of four segmentation results (AveSeg) showed better or comparable results in accuracy for most cases with respect to ConSeg. AP, AveSeg and ConSeg presented better RE and DSC in irregular masks as compared with rectangle masks. Additionally, all methods underestimated the tumour boundaries in relation to the ground truth for XCAT including respiratory motion.Conclusions:The consensus method could be a robust approach to alleviate segmentation variabilities, but did not seem to improve the accuracy of segmentation results on average. Irregular initial masks might be at least in some cases attributable to mitigate the segmentation variability as well.

Using Blockchain to Achieve Decentralized Privacy in IoT Healthcare

With the advent of the Internet of Things (IoT), e-health has become one of the main topics of research. Due to the sensitivity of patient information, patient privacy seems challenging. Nowadays, patient data is usually stored in the cloud in healthcare programs, making it difficult for users to have enough control over their data. The recent increment in announced cases of security and surveillance breaches compromising patients' privacy call into question the conventional model, in which third-parties gather and control immense amounts of patients' Healthcare data. In this work, we try to resolve the issues mentioned above by using blockchain technology. We propose a blockchain-based protocol suitable for ehealth applications that does not require trust in a third party and provides an efficient privacypreserving access control mechanism. Transactions in our proposed system, unlike Bitcoin, are not entirely financial, and we do not use conventional methods for consensus operations in blockchain like Proof of Work (PoW). It is not suitable for IoT applications because IoT devices have resourcesconstraints. Usage of appropriate consensus method helps us to increase network security and efficiency, as well as reducing network cost, i.e., bandwidth and processor usage. Finally, we provide security and privacy analysis of our proposed protocol.

Relationship Between Nurses’ Ethical Sensitivity and Work Environment Index

Providing nursing care based on ethical values ​​requires nurses to have ethical sensitivity. Determining the factors related to moral sensitivity can help to identify strategies to promote this ability. The aim of this study was to investigate the relationship between nurses' ethical sensitivity and work environment index. This descriptive-correlational study was performed in the presence of 135 nurses of Vali-e-Asr Hospital in Shahr-e-Babak (Kerman province) in 2021. Samples were selected by consensus method. Lutzen Ethical Sensitivity Questionnaire and Work Environment Index Questionnaire were used to data collection. Data analysis was performed by determining the Pearson correlation coefficient and regression test using SPSS26. T-test and ANOVA were also used to examine the difference in mean scores based on demographic and occupational variables. Findings showed that the status of ethical sensitivity and work environment index of nurses participating in this study is at a moderate level and there is a significant correlation between nurses' ethical sensitivity and work environment index (P=0/049 R=0/212). Also, the mean score of work environment index showed a significant difference based on the variables of position (P=0/008) and shift work (P = 0/012). The existence of a significant relationship between nurses' ethical sensitivity and their work environment index confirms the importance of the work environment and its related consequences. According to the results of the present study, by optimizing the working environment of nurses, we can expect their ethical sensitivity to improve.

Dynamic edge event‐triggered consensus for one‐sided Lipschitz multiagent systems with disturbances

SummaryThis paper studies the consensus problem for one‐sided Lipschitz (OSL) multiagent systems (MASs) affected by bounded disturbances via dynamic edge event‐triggered mechanism (DEETM). Due to disturbances and OSL dynamics in MASs, the event‐triggered consensus problem of which is quite more difficult than those of linear or Lipschitz nonlinear networks without disturbances in the previous works. In order to cope with this problem, a novel consensus method is developed based on DEETM and adaptive technique, which contains adaptive gains in both controller and DEETM. Firstly, smooth adaptive items in controller are designed to significantly compensate unknown disturbances, which can guarantee asymptotic consensus. Moreover, DEETM can effectively reduce the communication cost among neighboring agents, which does not cause Zeno behavior. It is worth noticing that the developed method is fully distributed, which relies on neither global information of the communication topology nor the upper and lower bounds of disturbances. Finally, a simulation example is given to demonstrate the effectiveness of theoretical results.

A Progressive Plane Detection Filtering Method for Airborne LiDAR Data in Forested Landscapes

Ground filtering is necessary in processing airborne light detection and ranging (LiDAR) point clouds for forestry applications. This study proposes a progressive plane detection filtering (PPDF) method. First, the method uses multi-scale planes to characterize terrain, i.e., the local terrain with large slope variations is represented by small-scale planes, and vice versa. The planes are detected in local point clouds by the random sample consensus method with decreasing plane sizes. The reliability of the planes to represent local terrain is evaluated and the planes with optimal sizes are selected according to evaluation results. Then, ground seeds are identified by selecting the interior points of the planes. Finally, ground points are iteratively extracted based on the reference terrain, which is constructed using evenly distributed neighbor ground points. These neighbor points are identified by selecting the nearest neighbor points of multiple subspaces, which are divided from the local space with an unclassified point as center point. PPDF was tested in six sites with various terrain and vegetation characteristics. Results showed that PPDF was more accurate and robust compared to the classic filtering methods including maximum slope, progressive morphology, cloth simulation, and progressive triangulated irregular network densification filtering methods, with the smallest average total error and standard deviation of 3.42% and 2.45% across all sites. Moreover, the sensitivity of PPDF to parameters was low and these parameters can be set as fixed values. Therefore, PPDF is effective and easy-to-use for filtering airborne LiDAR data.

Track forecast: Operational capability and new techniques - Summary from the Tenth International Workshop on Tropical Cyclones (IWTC-10)

In this paper, we summarize findings from the Tenth International Workshop on Tropical Cyclones (IWTC-10) subgroup on operational track forecasting techniques and capability.The rate of improvement in the accuracy of official forecast tracks (OFTs) appears to be slowing down, at least for shorter lead times, where we may be approaching theoretical limits. Operational agencies continue to use consensus methods to produce the OFT with most continuing to rely on an unweighted consensus of four to nine NWP models. There continues to be limited use of weighted consensus techniques, which is likely a result of the skills and additional maintenance needed to support this approach. Improvements in the accuracy of ensemble mean tracks is leading to increased use of ensemble means in consensus tracks.Operational agencies are increasingly producing situation-dependent depictions of track uncertainty, rather than relying on a static depiction of track forecast certainty based on accuracy statistics from the preceding 5 years. This trend has been facilitated by the greater availability of ensemble NWP guidance, particularly vortex parameter files, and improved spread in ensembles. Despite improving spread-skill relationships, most ensemble NWP systems remain under spread. Hence many operational centers are looking to leverage “super-ensembles” (ensembles of ensembles) to ensure the full spread of location probability is captured. This is an important area of service development for multi-hazard impact-based warnings as it supports better decision making by emergency managers and the community in the face of uncertainty.

Development and validation of two versions of the Nurse-Patient Mutuality in Chronic Illness scale.

The aim of the study was to develop and psychometrically test the Nurse-Patient Mutuality in Chronic Illness (NPM-CI) scale in two versions, one for nurses and one for patients. A multiphase methodological study was conducted. In the first phase, a qualitative investigation was conducted through interviews and an analysis of the content; inductively, the items of two instruments were generated, one for nurses and one for patients. In the second phase, the content and face validity were assessed through the expert consensus method. In the third phase, exploratory factor analysis (EFA), Cronbach's alpha test, intraclass correlation and Pearson correlation coefficients were conducted to estimate construct, criterion validity and instrument reliability. For each phase, the sample included nurses and patients recruited from a large hospital in Northern Italy. Data collection was conducted between June and September 2021. Nurse and patient versions of the NPM-CI scale were developed. Two rounds of consensus reduced the items from 39 to 20; content validity index ranged between 0.78 and 1, content validity ratio was 0.94. Face validity indicated clarity and comprehensibility of the items. EFA identified three latent factors for both the scales. Internal consistency was satisfactory, with Cronbach's alphas ranging between .80 and .90. Test-retest stability was suggested, with an intraclass correlation coefficient of .96 (nurse scale) and .97 (patient scale). Predictive validity was established, with a Pearson correlation coefficient of .43 (nurse scale) and 0.55 (patient scale) between the mutuality scales and satisfaction in providing and receiving care. The results suggest that the NPM-CI scales are sufficiently valid and reliable for the clinical practice among chronic illness patients and the nurses caring for them. A more in-depth exploration of this construct in the context of nursing and patient outcomes is warranted. Patients were involved in all study phases. Mutuality is fundamental in the relationship between nurse and patient, based on trust, equality, reciprocity, and mutual respect. The NPM-CI scale was developed and psychometric estimated through a multiphase study in both nurse and patient versions. The NPM-CI scale measures the factors of 'developing and going beyond', 'being the point of reference' and 'deciding and sharing care'. The NPM-CI scale allows us to measure mutuality in clinical practice and research. Expected outcomes and influencing factors for patients and nurses could be associated.

Using a consensus meeting to enhance fracture care education in low income countries

BackgroundA key strategy to building surgical capacity in low income countries involves training care providers, particularly in the interventions highlighted by the Lancet Commission for Global Surgery, including the management of open fractures. This is a common injury, especially in areas with a high incidence of road traffic incidents. The aim of this study was to use a nominal group consensus method to design a course on open fracture management for clinical officers in Malawi.MethodsThe nominal group meeting was held over two days, including clinical officers and surgeons from Malawi and the UK with various levels of expertise in the fields of global surgery, orthopaedics and education. The group was posed with questions on course content, delivery and evaluation. Each participant was encouraged to suggest an answer and the advantages and disadvantages of each suggestion were discussed before voting through an anonymous online platform. Voting included use of a Likert scale or ranking available options. Ethical approval for this process was obtained from the College of Medicine Research and Ethics Committee Malawi and the Liverpool School of Tropical Medicine.ResultsAll suggested course topics received an average score of greater than 8 out of 10 on a Likert scale and were included in the final programme. Videos was the highest ranking option as a method for delivering pre-course material. The highest ranking methods for each course topic included lectures, videos and practicals. When asked what practical skill should be tested at the end of the course, the highest ranking option was “initial assessment”.ConclusionThis work outlines how a consensus meeting can be used to design an educational intervention to improve patient care and outcomes. Through combining the perspectives of both the trainer and trainee, the course aligns both agendas so that it is relevant and sustainable.

Prescribed Settling Time Adaptive Neural Network Consensus Control of Multiagent Systems with Unknown Time-Varying Input Dead-Zone

For a class of multiagent systems with an unknown time-varying input dead-zone, a prescribed settling time adaptive neural network consensus control method is developed. In practical applications, some control signals are difficult to use effectively due to the extensive existence of an input dead-zone. Moreover, the time-varying input gains further seriously degrade the performance of the systems and even cause system instability. In addition, multiagent systems need frequent communication to ensure a system’s consistency. This may lead to communication congestion. To solve this problem, an event-triggered adaptive neural network control method is proposed. Further, combined with the prescribed settling time transform function, the developed consensus method greatly increases the convergence rate. It is demonstrated that all followers of multiagent systems can track the virtual leader within a prescribed time and not exhibit Zeno behavior. Finally, the theoretical analysis and simulation verify the effectiveness of the designed control method.

Developing key performance indicators for adult critical care transfer services: Scoping review and Delphi technique.

In 2021 NHS England commissioned regional Adult Critical Care Transfer Services. These services will replace a historically predominant ad hoc approach to adult critical care transfers nationally. It is anticipated that these new formal services will provide a system of robust regional & national governance previously acknowledged to be deficient. As part of this process, it is important that an agreed set of transfer service quality indicators are developed to drive equitable improvement in patient care. We used a Delphi technique to develop a set of key performance indicators through consensus for a recently established London critical care transfer service. We believe this may be the first-time key performance indicators have been developed for adult critical care transfer services using a consensus method. We hope services will consider tracking similar measures to enable benchmarking and drive improvements in patient care.

Design and implementation of the food supply chain traceability system based on the blockchain

Traditional enterprise-level food supply chain traceability systems are notoriously expensive and complex to set up, but there is a solution that can successfully address both of these problems: the food supply chain traceability system. This study, on the other hand, suggests a blockchain-based food supply chain traceability system business system. The paper is concerned with the food supply chain traceability system. The authentication and authorization procedures, traffic scheduling, and settlement monitoring are examples of some of the operations that make use of the blockchain infrastructure. In the meanwhile, the throughput of the system is increased by making improvements to the consensus method. Users of this system can provide customized service needs for food supply chain traceability systems by using this system. The issue of locking up the system in the food supply chain traceability system is successfully solved by the system, which embodies a great deal of application potential inside the food supply chain traceability system.

Development and Validation of the Morphea Activity Measure in Patients With Pediatric Morphea

Morphea is an insidious inflammatory disorder of the skin and deeper tissues. Determining disease activity is challenging yet important to medical decision-making and patient outcomes. To develop and validate a scoring tool, the Morphea Activity Measure (MAM), to evaluate morphea disease activity of any type or severity that is easy to use in clinical and research settings. This pilot diagnostic study was conducted from September 9, 2019, to March 6, 2020, in 2 phases: development and validation. During the development phase, 14 morphea experts (dermatologists and pediatric dermatologists) used a Delphi consensus method to determine items that would be included in the MAM. The validation phase included 8 investigators who evaluated the tool in collaboration with 14 patients with pediatric morphea (recruited from a referral center [Medical College of Wisconsin]) during a 1-day in-person meeting on March 6, 2020. During the development phase, online survey items were evaluated by experts in morphea using a Likert scale (score range, 0-10, with 0 indicating not important and 10 indicating very important); agreement was defined as a median score of 7.0 or higher, disagreement as a median score of 3.9 or lower, and no consensus as a median score of 4.0 to 6.9. During the validation phase, reliability (interrater and intrarater agreement using intraclass correlation coefficients), validity (using the content validity index and κ statistics as well as correlations with the modified Localized Scleroderma Severity Index and the Physician Global Assessment of Activity using Spearman ρ coefficients), and viability (using qualitative interviews of investigators who used the MAM tool) were evaluated. Descriptive statistics were used for quantitative variables. Data on race and ethnicity categories were collected but not analyzed because skin color was more relevant for the purposes of this study. Among 14 survey respondents during the development phase, 9 (64.3%) were pediatric dermatologists and 5 (35.7%) were dermatologists. After 2 rounds, a final tool was developed comprising 10 items that experts agreed were indicative of morphea activity (new lesion in the past 3 months, enlarging lesion in the past 3 months, linear lesion developing progressive atrophy in the past 3 months, erythema, violaceous rim or color, warmth to the touch, induration, white-yellow or waxy appearance, shiny white wrinkling, and body surface area). The validation phase was conducted with 14 patients (median age, 14.5 years [range, 8.0-18.0 years]; 8 [57.1%] female), 2 dermatologists, and 6 pediatric dermatologists. Interrater and intrarater agreement for MAM total scores was good, with intraclass correlation coefficients of 0.844 (95% CI, 0.681-0.942) for interrater agreement and 0.856 (95% CI, 0.791-0.901) for intrarater agreement. Correlations between the MAM and the modified Localized Scleroderma Severity Index (Spearman ρ = 0.747; P < .001) and the MAM and the Physician Global Assessment of Activity (Spearman ρ = 0.729; P < .001) were moderately strong. In qualitative interviews, evaluators agreed that the tool was easy to use, measured morphea disease activity at a single time point, and should be responsive to changes in morphea disease activity over multiple time points. In this study, the MAM was found to be a reliable, valid, and viable tool to measure pediatric morphea activity. Further testing to assess validity in adults and responsiveness to change is needed.

Identifying patients with psychosocial problems in general practice: A scoping review.

We conducted a scoping review with the aim of comprehensively investigating what tools or methods have been examined in general practice research that capture a wide range of psychosocial problems (PSPs) and serve to identify patients and highlight their characteristics. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and the Joanna Briggs Institute Reviewer's Manual on scoping reviews. A systematic search was conducted in four electronic databases (Medline [Ovid], Web of Science Core Collection, PsycInfo, Cochrane Library) for quantitative and qualitative studies in English, Spanish, French, and German with no time limit. The protocol was registered with Open Science Framework and published in BMJ Open. Of the 839 articles identified, 66 met the criteria for study eligibility, from which 61 instruments were identified. The publications were from 18 different countries, with most studies employing an observational design and including mostly adult patients. Among all instruments, 22 were reported as validated, which we present in this paper. Overall, quality criteria were reported differently, with studies generally providing little detail. Most of the instruments were used as paper and pencil questionnaires. We found considerable heterogeneity in the theoretical conceptualisation, definition, and measurement of PSPs, ranging from psychiatric case findings to specific social problems. This review presents a number of tools and methods that have been studied and used in general practice research. Adapted and tailored to local circumstances, practice populations, and needs, they could be useful for identifying patients with PSPs in daily GP practice; however, this requires further research. Given the heterogeneity of studies and instruments, future research efforts should include both a more structured evaluation of instruments and the incorporation of consensus methods to move forward from instrument research to actual use in daily practice.

EBMICQL: Improving Efficiency of Blockchain Miner Pools via Incremental and Continuous Q-Learning Framework

Blockchain mining pools assist in reducing computational load on individual miner nodes via distributing mining tasks. This distribution must be done in a non-redundant manner, so that each miner is able to calculate block hashes with optimum efficiency. To perform this task, a wide variety of mining optimization methods are proposed by researchers, and most of them distribute mining tasks via statistical request processing models. These models segregate mining requests into non-redundant sets, each of which will be processed by individual miners. But this division of requests follows a static procedure, and does not consider miner specific parameters for set creation, due to which overall efficiency of the underlying model is limited, which reduces its mining performance under real-time scenarios. To overcome this issue, an Incremental &amp; Continuous Q-Learning Framework for generation of miner-specific task groups is proposed in this text. The model initially uses a Genetic Algorithm (GA) method to improve individual miner performance, and then applies Q-Learning to individual mining requests. The Reason for selecting GA model is that it assists in maintaining better speed-to-power (S2P) ratio by optimization of miner resources that are utilized during computations. While, the reason for selecting Q-Learning Model is that it is able to continuously identify miners performance, and create performance-based mining pools at a per-miner level. Due to application of Q-Learning, the model is able to assign capability specific mining tasks to individual miner nodes. Because of this capability-driven approach, the model is able to maximize efficiency of mining, while maintaining its QoS performance. The model was tested on different consensus methods including Practical Byzantine Fault Tolerance Algorithm (PBFT), Proof-of-Work (PoW), Proof-of-Stake (PoS), and Delegated PoS (DPoS), and its performance was evaluated in terms of mining delay, miner efficiency, number of redundant calculations per miner, and energy efficiency for mining nodes. It was observed that the proposed GA based Q-Learning Model was able to reduce mining delay by 4.9%, improve miners efficiency by 7.4%, reduce number of redundant computations by 3.5%, and reduce energy required for mining by 7.1% when compared with various state-of-the-art mining optimization techniques. Similar performance improvement was observed when the model was applied on different blockchain deployments, thus indicating better scalability and deployment capability for multiple application scenarios.

Monotone decreasing LKF method for secure consensus of second-order mass with delay and switching topology

Monotone decreasing LKF method for secure consensus of second-order mass with delay and switching topology

Comparison of risk assessments for venous thromboembolism during the puerperium

ObjectiveTo compare the predictive value of different risk assessment methods for puerperium venous thromboembolism (VTE). MethodsThis study included 55 women with and 165 women without puerperal VTE. Using the cases, 11 assessment methods were compared. ResultsThe area under the curve (AUC) value of the 11 assessments was highest for the modified Caprini risk assessment model for pregnancy (a modified risk scoring method from Caprini, AUC = 0.805). Pairwise comparison of the AUC values of the 11 assessment methods indicated no significant difference among the five methods with AUC values > 0.7. Among them, the modified Caprini, the risk scoring method recommended by the Swedish Guidelines (Swedish method), and the risk scoring method recommended by the Shanghai consensus (Shanghai method) performed better than the other six methods with AUC values < 0.7 (P < 0.05). The sensitivities of the five methods for predicting a high risk of VTE were 69.09–94.55% and the specificities were 25.45–77.58%. The sensitivity of the modified Caprini was higher than those of the risk management method from the Chinese consensus (Chinese consensus method), Royal College of Obstetricians and Gynaecologists risk assessment scale (RCOG), and Swedish method (P < 0.05), but the specificity was only 25.45%. No significant difference in sensitivity was detected among the Swedish, Shanghai, RCOG, and Chinese consensus methods, whereas the specificity of the Swedish method was higher than that of the Shanghai, RCOG, and Chinese consensus methods. ConclusionThe predictive value of different risk assessment methods for puerperium VTE varies greatly. Considering the sensitivity and specificity, the Swedish method may have better clinical application value among the 11 methods.

A novel method for the translation and cross-cultural adaptation of health-related quality of life patient-reported outcome measurements

BackgroundThis paper presents a novel methodology for translation and cross-cultural adaptation of health-related quality-of-life patient-reported outcome measures, incorporating the Delphi method. Specifically, we describe the process of translating the Pelvic Floor Distress Inventory-20 and Pelvic Floor Impact Questionnaire-7 from English to Norwegian using this method.MethodsThe multistep translation method combined the European Organization for Research and Treatment of Cancer Quality of Life guidelines, an Expert Panel review, and the Delphi method. It comprised two independent forward- and back-translations. While the bilingual pelvic floor Expert Panel ensured rigorous cross-checking and effective cross-cultural adaptation, the addition of the Delphi method (comprising the attributes of anonymity, controlled feedback, and statistical group response) further established consensus on translated items.OutcomesThe application of the Delphi method in the Expert Panel phase proved adequate in producing comprehensible intermediate Norwegian versions ready for pilot testing. The Expert Panel reviewed the comments made by patients completing the instruments and offered advice to allow final translated versions to be produced and tested for measurement properties. This iterative approach, internal logic, and anonymity between rounds improved the evaluations that the panel members provided, which in turn enhanced the final translated Patient Reported Outcome Measures (PROMs).ConclusionsTo our knowledge, this work represents the first demonstration of the application of an Expert Panel review incorporating a Delphi method to assess health-related quality-of-life instruments. The controlled feedback approach, iterative nature, internal logic, and anonymity of the Delphi consensus method appeared to ensure a good cross-cultural adaptation of these PROMs.