Selective Search Research Articles

Performance indicators in long-term care for older people in 38 countries: a scoping review

Abstract Background A large number of performance indicators in long-term care have been developed and tested. However, the use of such indicators for regular monitoring of long-term care at regional and national level presents a number of challenges related to coordination of stakeholders, regulation and data collection capabilities. Our aim was to review the performance indicators in long-term care that are currently in use for regular monitoring by 38 OECD member states. Methods We used a scoping review approach that included a systematic search of scientific publications, review of multi-country reports and an internet search of relevant websites of the countries included. The scoping review protocol was registered before starting paper selection and web searches. Results We retrieved 12.776 papers, of which 10 were kept after selection for data extraction. Preliminary results of the review of reports and internet resources led to the retrieval of 399 performance indicators in long-term care from 24 OECD member states, while no indicators could be found for 14 member states. Performance indicators most often relate to falls, pressure ulcers, unplanned weight loss and use of restraints, which are collected by 7 or more of the 24 countries. The definitions of the indicators differ considerably between countries, as well as the definitions of long-term care and hence the settings in which these indicators are used. Conclusions Consideration of indicators often used in several countries may be of interest to decision-makers establishing or updating indicator sets in this area. There is considerable heterogeneity in the nature, definitions and settings of long-term care performance indicators used by OECD member states. These differences are a significant challenge to establish international comparability of the indicators reported. Key messages • The indicators most frequently used for regular monitoring of long-term care performance are related to falls, pressure ulcer, unplanned weight loss and use of restraints. • There is considerable heterogeneity in the nature, definitions and settings of long-term care performance indicators used in different countries, which make comparisons of results challenging.

Extracorporeal Cardiopulmonary Resuscitation.

Around the world, survival rates after cardiac arrest range between <14% for in-hospital (IHCA) and <10% for outof- hospital cardiac arrest (OHCA). This situation could potentially be improved by using extracorporeal membrane oxygenation (ECMO) during cardiopulmonary resuscitation (CPR), i.e. by extracorporeal cardiopulmonary resuscitation (ECPR). A selective literature search of Pubmed and Embase using the searching string ((ECMO) OR (ECLS)) AND (ECPR)) was carried out in February 2023 to prepare an up-to-date review of published trials comparing the outcomes of ECPR with those of conventional CPR. Out of 573 initial results, 12 studies were included in this review, among them three randomized controlled trials comparing ECPR with CPR, involving a total of 420 patients. The survival rates for ECPR ranged from 20% to 43% for OHCA and 20% to 30.4% for IHCA. Most of the publications were associated with a high degree of bias and a low level of evidence. ECPR can potentially improve survival rates after cardiac arrest compared to conventional CPR when used in experienced, high-volume centers in highly selected patients (young age, initial shockable rhythm, witnessed cardiac arrest, therapy-refractory high-quality CPR). No general recommendation for the use of ECPR can be issued at present.

Autonomous Parking Space Detection for Electric Vehicles Based on Improved YOLOV5-OBB Algorithm

Currently, in the process of autonomous parking, the algorithm detection accuracy and rate of parking spaces are low due to the diversity of parking scenes, changes in lighting conditions, and other unfavorable factors. An improved algorithm based on YOLOv5-OBB is proposed to reduce the computational effort of the model and increase the speed of model detection. Firstly, the backbone module is optimized, the Focus module and SSP (Selective Spatial Perception) module are replaced with the general convolution and SSPF (Selective Search Proposals Fusion) modules, and the GELU activation function is introduced to reduce the number of model parameters and enhance model learning. Secondly, the RFB (Receptive Field Block) module is added to fuse different feature modules and increase the perceptual field to optimize the small target detection. After that, the CA (coordinate attention) mechanism is introduced to enhance the feature representation capability. Finally, the post-processing is optimized using spatial location correlation to improve the accuracy of the vehicle position and bank angle detection. The implementation results show that by using the improved method proposed in this paper, the FPS of the model is improved by 2.87, algorithm size is reduced by 1 M, and the mAP is improved by 8.4% on the homemade dataset compared with the original algorithm. The improved model meets the requirements of perceived accuracy and speed of parking spaces in autonomous parking.

Individualisation, personalisation and person-centredness in mental healthcare: a scoping review of concepts and linguistic network visualisation

BackgroundTargeted mental health interventions are increasingly described as individualised, personalised or person-centred approaches. However, the definitions for these terms vary significantly. Their interchangeable use prevents operationalisations and measures.ObjectiveThis scoping review...

Meralgia Paresthetica: Relevance, Diagnosis, and Treatment.

Pain and sensory disturbance in the distribution of the lateral femoral cutaneous nerve in the ventrolateral portion of the thigh is called meralgia paresthetica (MP). The incidence of MP has risen along with the increasing prevalence of obesity and diabetes mellitus and was recently estimated at 32 new cases per 100 000 persons per year. In this review, we provide an overview of current standards and developments in the diagnosis and treatment of MP. This review is based on publications retrieved by a selective literature search, with special attention to meta-analyses, systematic reviews, randomized and controlled trials (RCTs), and prospective observational studies. The diagnosis is mainly based on typical symptoms combined with a positive response to an infiltration procedure. In atypical cases, electrophysiological testing, neurosonography, and magnetic resonance imaging can be helpful in establishing the diagnosis. The literature search did not reveal any studies of high quality. Four prospective observational studies with small case numbers and partly inconsistent results are available. In a meta-analysis of 149 cases, pain relief was described after infiltration in 85% of cases and after surgery in 80%, with 1-38 months of follow-up. In another meta-analysis of 670 cases, there was pain relief after infiltration in 22% of cases, after surgical decompression in 63%, and after neurectomy in 85%. Hardly any data are available on more recent treatment options, such as radiofrequency therapy, spinal cord stimulation, or peripheral nerve stimulation. The state of the evidence is limited in both quantity and quality, corresponding to evidence level 2a for surgical and non-surgical methods. Advances in imaging and neurophysiological testing have made the diagnosis easier to establish. When intervention is needed, good success rates have been achieved with surgery (decompression, neurectomy), and variable success rates with infiltration.

Dynamic Parameter Optimization of High-Speed Pantograph Based on Swarm Intelligence and Machine Learning

Good pantograph–catenary interaction quality is a fundamental premise for ensuring stable and reliable current collection of high-speed trains, and the optimization of dynamic parameters of high-speed pantographs provides an effective approach to improve the current collection quality of the pantograph–catenary system. In this paper, with the objective of minimizing the standard deviation of the pantograph–catenary contact force, the multi-parameter joint optimization for pantograph at different filtering frequencies and running speeds was carried out by using swarm intelligence optimization algorithm and artificial neural network method. First, the selection operator in genetic algorithm (GA) was introduced into crow search algorithm (CSA), and the selective CSA was proposed, which can effectively improve the solution accuracy and convergence rate of multi-parameter optimization. Second, a radial basis function (RBF) neural network was used to construct a surrogate model of the standard deviation of contact force with respect to the running speed and pantograph dynamic parameters, and a method for optimizing the upper limit of mapping interval of the decision variables by the selective crow search algorithm (SCSA) was proposed, which effectively improves the generalization ability of the surrogate model. Finally, by combining the surrogate model and SCSA, optimization iterations for a total of 630 combined conditions such as cut-off frequency, running speed and pantograph dynamic parameters were conducted, and an optimization method for high-speed pantograph dynamic parameters with universal applicability was proposed.

ReliefF based feature selection and Gradient Squirrel search Algorithm enabled Deep Maxout Network for detection of heart disease

Detecting heart disease is challenging in clinical settings, leading to an increase in mortality rates. Current detection processes often rely on Electrocardiography (ECG) signal analysis, which requires accurate data processing and feature extraction. Traditional methods have limitations like processing time and accuracy. To address these issues, a novel approach called Gradient Squirrel Search Algorithm-Deep Maxout Network (GSSA-DMN) is proposed for more effective heart disease detection. The proposed GSSA-DMN approach involves several steps. Initially, input data is obtained from a specific database and subjected to data pre-processing, including log scaling for pattern transformation. Feature selection is then performed using ReliefF on the pre-processed data. The core of the approach lies in the Deep Maxout Network (DMN) trained by the Gradient Squirrel Search Algorithm (GSSA), which combines Gradient Descent Optimization (GDO) with the Squirrel Search Algorithm (SSA). The GSSA-DMN approach demonstrates remarkable performance. It achieves high accuracy, sensitivity, and specificity values of approximately 93.2%, 93%, and 91.5%, respectively. These results indicate its effectiveness in heart disease detection.Comparatively, the proposed GSSA-DMN method outperforms existing techniques. Its accuracy surpasses those of other methods by margins of 6.97%, 5.79%, 4.50%, 3.43%, and 1.93% when compared to BF-PSO, Bi-LSTM-CRF, XGBoost, RLNNC, and DMOA-SqueezeNet for K-value. This suggests that GSSA-DMN provides superior accuracy in detecting heart disease. In summary, the GSSA-DMN approach presents a promising solution for improving the accuracy and efficiency of heart disease detection compared to traditional methods and existing state-of-the-art techniques.

Methods of Conservative Intra-Articular Treatment for Osteoarthritis of the Hip and Knee.

Osteoarthritis is a degenerative joint disease that is becoming increasingly common as the population ages. Conservative treatment for hip or knee osteoarthritis has been limited to pain control. Intra-articular injections for targeted local treatment have been widely used in clinical practice for many years. This review is based on publications retrieved by a selective literature search, including recent meta-analyses, systematic reviews, randomized controlled trials (RCTs), and current guidelines. In Germany, the 12-month prevalence of osteoarthritis in adults is 17.9%. Conservative treatments are intended to alleviate symptoms and do not affect the progression of the disease. Glucocorticoids can be used to relieve otherwise intractable pain in the short term, but their prolonged use increases the risk of cartilage loss and progression of osteoarthritis. According to multiple guidelines, there is only weak evidence for the use of hyaluronic acid. Evidence does exist that high-molecular-weight hyaluronic acid may lead to better outcomes than the low-molecular-weight form. RCTs have revealed no more than short-term clinical efficacy for a variety of specific therapeutic approaches, including the use of cytokine inhibitors. Other treatments, e.g., with platelet-enriched plasma, aspirates from bone marrow or adipose tissue, or expanded mesenchymal stromal cells (MSC), have not been found to have clinically relevant long-term effects. In view of the scant available evidence, further standardized RCTs will be needed to give a more comprehensive picture of the efficacy of intra-articular treatments for hip and knee osteoarthritis.

Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture

Plant health plays an important role in influencing agricultural yields and poor plant health can lead to significant economic losses. Grapes are an important and widely cultivated plant, especially in the southern regions of Russia. Grapes are subject to a number of diseases that require timely diagnosis and treatment. Incorrect identification of diseases can lead to large crop losses. A neural network deep learning dataset of 4845 grape disease images was created. Eight categories of common grape diseases typical of the Black Sea region were studied: Mildew, Oidium, Anthracnose, Esca, Gray rot, Black rot, White rot, and bacterial cancer of grapes. In addition, a set of healthy plants was included. In this paper, a new selective search algorithm for monitoring the state of plant development based on computer vision in viticulture, based on YOLOv5, was considered. The most difficult part of object detection is object localization. As a result, the fast and accurate detection of grape health status was realized. The test results showed that the accuracy was 97.5%, with a model size of 14.85 MB. An analysis of existing publications and patents found using the search “Computer vision in viticulture” showed that this technology is original and promising. The developed software package implements the best approaches to the control system in viticulture using computer vision technologies. A mobile application was developed for practical use by the farmer. The developed software and hardware complex can be installed in any vehicle. Such a mobile system will allow for real-time monitoring of the state of the vineyards and will display it on a map. The novelty of this study lies in the integration of software and hardware. Decision support system software can be adapted to solve other similar problems. The software product commercialization plan is focused on the automation and robotization of agriculture, and will form the basis for adding the next set of similar software.

Heat in Germany: Health risks and preventive measures.

Climate change has already led to a significant temperature increase in Germany. The average temperature in the past decade was approximately 2°C above the pre-industrial level and eight of the ten hottest summers since the beginning of systematic weather records in 1881 were recorded in the last 30 years. Based on a selective literature search and authors' own results, the article summarises the current state of knowledge on heat and its health impacts for Germany, addresses adaptation measures, and gives an outlook on implementation and research questions. Heat can aggravate pre-existing conditions such as diseases of the cardiovascular system, the respiratory tract, or the kidneys and trigger potentially harmful side effects for numerous medications. A significant increase in mortality is regularly observed during heat events. Previous approaches to mitigate the health impact of high temperatures include, for example, the heat alerts of the German Meteorological Service and recommendations for the preparation of heat-health action plans. Evidence on health impacts of heat and awareness of the need for heat-related health protection have grown in recent years, but there is still a need for further action and research.

ADVANCES IN EARLY DETECTION OF OPTICAL NEURITIS DIAGNOSIS SYSTEMS

Optic neuritis (ON) is an inflammatory demyelinating disorder of the optic nerve, which can be the first manifestation of multiple sclerosis (MS). In this paper we review the recent advances in early diagnosis of the disorder focusing primarily on the techniques : Retinal OCT Texture Analysis, Machine learning based on Color Fundus Images and Optical Coherence Tomography. This review is based on articles retrieved by a selective search of the PubMed database and other related journal articles, on the pertinent guidelines, and on the authors’ clinical experience. Throughout, we present tables that summarize and draw distinctions among key ideas and approaches. Where available, we provide comparative analyses, and we make suggestions for analyses yet to be done.

Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems.

The emergence of the Internet of Medical Things (IoMT) has brought together developers from the Industrial Internet of Things (IIoT) and healthcare providers to enable remote patient diagnosis and treatment using mobile-device-collected data. However, the utilization of traditional AI systems raises concerns about patient privacy. To address this issue, we present a privacy-enhanced approach for illness diagnosis within the IoMT framework. Our proposed interoperable IoMT implementation focuses on optimizing IoT network performance, including throughput, energy consumption, latency, packet delivery ratio, and network longevity. We achieve these improvements using techniques such as device authentication, energy-efficient clustering, environmental monitoring using Circular-based Hidden Markov Model (C-HMM), data verification using Awad's Entropy-based Ten-Fold Cross Entropy Verification (TCEV), and data confidentiality using Twine-LiteNet-based encryption. We employ the Search and Rescue Optimization algorithm (SRO) for optimal route selection, and the encrypted data are securely stored in a cloud server. With extensive network simulations using ns-3, our approach demonstrates substantial enhancements in the specified performance metrics compared with previous works. Specifically, we observe a 20% increase in throughput, a 15% reduction in packet drop rate (PDR), a 35% improvement in network lifetime, and a 10% decrease in energy consumption and delay. These findings underscore the efficacy of our approach in enhancing IoT network interoperability and protection, fostering improved patient care and diagnostic capabilities.

Expected polynomial-time randomized algorithm for graph coloring problem

Given a graph G=(V,E) with n=|V| vertices, the graph coloring problem is defined as Find a color vectorC=(c(v)), where c(v)∈{1,2,…,n} denotes the color of vertex v∈V, such that no monochromatic edge exists in G and the span, the total number of distinct colors in C, is minimized. Since the problem is NP-complete, a greedy coloring is commonly used for solving it. Greedy coloring visits the vertices of G following an order S, and while visiting a vertex v, it puts the minimum color absent in all neighbors of v. We show that the orders producing span ≤k, where k≤n is a positive integer, can be partitioned into disjoint subsets of equivalent orders. Next, we propose a selective search (SS) algorithm, which takes ρ as an input parameter, selects ≥ρ orders each from a different set of equivalent orders with high probability, applies greedy coloring on them, and returns the color vector with minimum span. We analytically show that SS performs better than greedy coloring with high probability by evaluating the same number of orders. We propose an incremental search heuristic (ISH), which ρ1 times execute SS with parameter ρ2 and returns the color vector with minimum span. A parallel version of ISH called PISH is also proposed, executing ρ1 SS calls in parallel. We show that ISH hits an optimum coloring for a graph G=(V,E) with χ(G)(Δ(G)+1)≤ne in expected O(|V|+|E|) time and space complexities. We have evaluated ISH and PISH on 136 challenging benchmarks and shown that they significantly outperform 10 existing state-of-the-art algorithms. Finally, we validated our theoretical findings by evaluating ISH and greedy coloring on random graphs.

Perioperative Arrhythmias.

Perioperative arrhythmias are common depending on the type of the operation and can increase morbidity and mortality. This review is based on pertinent publications retrieved by a selective search in PubMed, as well as the relevant European guidelines. Arrhythmias are seen in more than 90% of cardiac operations; they are usually transient and often asymptomatic. The risk factors for arrhythmia include ion channel diseases, old age, structural heart disease, cardiac surgery, noncardiac surgery with major fluid shifts, and pulmonary resection. The full spectrum of supraventricular and ventricular arrhythmias can arise perioperatively. Correct ECG interpretation, consideration of the arrhythmia in the overall clinical context, and an understanding of its causes, pathophysiology, and options for effective treatment are critically important. According to a meta-analysis, betablockers lower the risk of perioperative atrial fibrillation (OR = 0.56; 95% confidence interval: [0.35; 0.91]). If anticoagulant treatment is not interrupted for surgery, there is less bleeding with direct oral anticoagulants than with vitamin K antagonists (relative risk: 0.62 [0.47; 0.82]). Moreover, clinical follow-up is important, especially for patients with new-onset atrial fibrillation or heart failure. The identification of high-risk patients and the provision of individualized perioperative monitoring are essential aspects of patient safety. Outpatient cardiological follow-up can improve outcomes.

Effects of the Pandemic on the Care of Patients With Colorectal Cancer.

During the COVID-19 pandemic, there was a decrease in the rates of diagnosis and treatment of cancer. However, only a few detailed analyses have been made to date regarding the effect of the pandemic on the care of cancer patients in Germany. Such studies are needed as the basis for well-founded recommendations on health-care delivery priorities during pandemics and other, comparable situations of crisis. This review is based on publications that were retrieved by a selective search of the literature for controlled studies from Germany on the effects of the pandemic on colonoscopies, first diagnoses of colorectal cancer (CRC), surgical procedures for CRC, and CRC-related mortality. Compared to 2019, the rate of screening colonoscopies performed by physicians in private practice was 1.6% higher in 2020 and 4.3% higher in 2021. On the other hand, the rate of diagnostic colonoscopies in the inpatient setting was 15,7% lower in 2020, while that of therapeutic colonoscopies was 11.7% lower. According to the data evaluated here, first diagnoses of CRC were 2.1% less common in January to September in 2020 than they had been in 2019; according to routine data collected by the statutory health insurance provider GRK, surgery for CRC was 10% less common in 2020 than in 2019. With regard to mortality, sufficient data from Germany were lacking to draw definite conclusions. International modeling data suggest an increase in mortality due to decreased colorectal screening rates during the pandemic that may at least be partially compensated for by intensified screening strategies following the pandemic. Three years after the onset of the COVID-19 pandemic, there is still only a limited evidence base for an evaluation of the effects of the pandemic on medical care and on the outcomes of patients with CRC in Germany. The implementation of central data and research infrastructures will be necessary for further study of the long-term effects of this pandemic, as well as to enable optimal preparedness for future crisis situations.

Qualitative evidence regarding the experience of receiving and providing care for mental health conditions in non-specialist settings in low-income and middle-income countries: a systematic review of reviews

QuestionThis review of reviews synthesises qualitative evidence on the experiences of receiving and providing care and treatment for mental health conditions in non-specialist settings in low-income and middle-income countries (LMICs),...

Individual Behaviour in Islamic Education Institutions

Individual behavior in Islamic educational institutions has been the subject of significant research in order to understand the factors that influence student learning and development in Islamic educational settings. The purpose of this study is to present a literature study that analyzes literature related to individual behavior in the context of Islamic educational institutions. This literature study uses a systematic and selective search method in identifying articles, journals, books, and other sources related to relevant topics. The focus of this research is on individual behavior which includes aspects such as academic behavior, social behavior, moral and religious behavior, as well as factors that influence individual behavior in Islamic educational institutions. The results of this literature study reveal that individual academic behavior in Islamic educational institutions involves factors such as learning motivation, active participation in learning, discipline, and responsibility for schoolwork. Individual social behavior includes positive interactions between students, teachers, and administrative staff, as well as concern for the school environment and the community. Meanwhile, individual moral and religious behavior involves understanding and practicing Islamic values, ethics, and integrity in daily life. Factors that influence individual behavior in Islamic educational institutions identified in this literature study include family and environmental influences, educational approaches applied in educational institutions, the teacher's role as a role model, as well as policies and norms that exist in the school environment. In conclusion, this literature study highlights the importance of understanding individual behavior in Islamic educational institutions. Previous research has identified factors that play a role in shaping positive and quality individual behavior in Islamic education environments. These findings provide important insights for Islamic education practitioners, teachers and related stakeholders to design appropriate strategies and interventions in promoting beneficial individual behavior in Islamic educational institutions.&#x0D;

OCRUN: An oppositional Runge Kutta optimizer with cuckoo search for global optimization and feature selection

The recently proposed swarm intelligence algorithm, Runge–Kutta Optimization (RUN), is rooted in the fourth-order Runge–Kutta method. Compared with its counterparts, RUN boasts an advantage of having a more concrete theoretical foundation embodying a more powerful optimization efficacy, free from any metaphor. However, RUN still has its shortcomings. The compelling enhanced solution function leads to insufficient exploration ability of the algorithm, and resulting in an imbalance between exploration and exploitation that cannot be mitigated. An improved version based on opposition-based learning and cuckoo search is proposed to compensate for the above deficiencies, called OCRUN. OCRUN is tested on 30 test functions of CEC2014 with 10 classical metaheuristics and 9 advanced metaheuristics, respectively. Combining the experimental results and the Wilcoxon signed-rank test, OCRUN exhibits excellent performance. At the same time, parameter sensitivity analysis experiments are also carried out on this test set. Furthermore, a binary implementation of the algorithm was constructed specifically for feature selection cases, labeled as BOCRUN. BOCRUN is compared with 5 existing binary metaheuristics on 15 public datasets. The experimental results show that the improved algorithm performs well in feature selection. Therefore, OCRUN is an effectively improved optimizer. Finally, the OCRUN method offers high-quality solutions to engineering problems and contributes significantly to engineering design under practical constraints. The method has been successfully applied to various design scenarios, such as reducer design, cantilever beam design, and tension/compression spring design. The OCRUN method outperforms other similar products in terms of performance and effectiveness.

Chronic Primary Pelvic Pain Syndrome in Men.

Chronic primary pelvic pain syndrome in men (CPPPSm) can be associated with urogenital pain, urinary symptoms, sexual dysfunction, and emotional disturbance. Its clinical heterogeneity and incompletely understood pathogenesis make it more difficult to treat. This article is intended to familiarize the reader with basic aspects of the manifestations, pathophysiology, diagnostic evaluation, differential diagnosis, and treatment of this condition. This article is based on relevant publications retrieved by a selective search of the literature, including the current guidelines of the European Association of Urology. The features of this disease pertaining to urology, psychosomatic medicine, and pain medicine are illuminated from an interdisciplinary perspective. Chronic pelvic pain appears to arise through a complex interaction of inflammatory, infectious, neurological, musculoskeletal, and psychosomatic factors. A comprehensive diagnostic work-up should be carried out to evaluate and exclude the numerous differential diagnoses. Treatment strategies are based on the clinical phenotype. Randomized controlled trials have shown that significant relief can be achieved with a variety of drugs and non-pharmacological treatments, selected according to the manifestations of the condition in the individual case. Attention must be paid to treatment-specific adverse effects. The management of patients with CPPPSm should consist of a comprehensive differential diagnostic evaluation and an individually oriented treatment strategy.

Mucinous tumors of the peritoneum

Disseminated peritoneal seeding of mucin-forming tumors is arare malignant disease with variable prognosis. Histomorphological criteria are instrumental in the prognostic assessment. The past 10years have led to astandardization of nomenclature and subsequently to the establishment of therapeutic standards. This article aims to provide the current status of the pathological classification, staging, and grading. Selective literature search in PubMed and Medline RESULTS: The vast majority of disseminated peritoneal mucinous diseases that correspond to the clinical presentation of pseudomyxoma peritonei (PMP) arise from mucinous tumors of the vermiform appendix. Here are to be distinguished: 1)low-grade appendiceal mucinous neoplasms (LAMN), 2)(very rare) high-grade appendiceal mucinous neoplasms (HAMN), 3)mucinous adenocarcinoma without signet ring cells (G2) and 4)mucinous adenocarcinoma with signet ring cells or signet ring cell carcinoma (G3). Other primary tumors only rarely induce PMP. Terms such as mucocele or mucinous cystadenoma of the appendix correspond to LAMN and should no longer be used. Prognostic distinctions are further made between low-grade PMP, which usually arises from LAMN, and the prognostically less favorable high-grade PMP, which usually arises from mucinous/signet ring cell adenocarcinoma or the rare HAMN. Disseminated peritoneal mucinous disease/PMP must then be further distinguished from prognostically excellent local mucin formation of the peri-appendix region. The currently valid nomenclature, as it has emerged from consensus meetings and in parts has also found its way into the current WHO 2019, has significantly contributed to the fact that the prognosis of patients today can be better estimated and effective forms of treatment could be developed.