Related Problems Research Articles

Prevalence of skin problems caused by insulin pump therapy and associated factors in children with type 1 diabetes mellitus: A large cross-sectional survey in China

AimsTo document the prevalence of skin problems associated with insulin pump use and identify contributing factors among children with type 1 diabetes mellitus in China. MethodsIn total, 461 children were recruited from an online community (i.e., a Wechat group) of pediatric patients with T1DM. A self-developed questionnaire was filled in by parents, collecting the information on social demographics, disease, and insulin pump therapy related characteristics and skin problems. We applied the Mann–Whitney U test, Chi square test and logistic regression analysis to identify the factors associated with skin problems. ResultsOf the 461 responders, 308 (66.8 %) children were reported to have skin problems. More specifically, 38.8 % had pigmentation changes, 22.3 % allergy/dermatitis, 20.2 % scaring, 11.5 % pain, 10.8 % infection, 10.6 % subcutaneous lipohypertrophy, and 6.1 % lipoatrophy. Logistic regression analysis showed that independent associated factors of skin problems were the caregiver’s educational level as college or above, patient having skin allergies, and using the Brand 2 insulin pump (p values < 0.05). ConclusionsThe present study documents the prevalence of skin problems and identifies associated factors, such as caregiver’s education, patients skin allergies, and using a specific brand of pump. Health education should address these factors in addition to the traditionally emphasized factors.

Neural Network-Based Modeling of Diffusion-Induced Stress in a Hollow Cylindrical Nano-Electrode of Lithium-Ion Battery

Abstract Understanding the interaction between mechanical deformation and mass transport, such as diffusion-induced stress, is crucial in the development of advanced battery materials and electrochemical devices. Mathematical modeling and solving the coupling problems have played important roles in advancing the understanding of the interaction between mechanical deformation and mass transport. As the complexity of mathematical modeling continues to increase, numerical methods used to solve the related coupling problems are likely to encounter significant challenges. This work explores the feasibility of designing a neural network specifically for solving diffusion-induced stress in the electrode of lithium-ion battery via deep learning techniques. A loss function is constructed from the spatiotemporal coordinates of sampling points within the solution domain, the overall structure of the system of partial differential equations, boundary conditions, and initial conditions. The distributions of stress and lithium concentration in a hollow-cylindrical nanoelectrode are obtained. The high degree of conformity between the numerical results and those from finite element method is demonstrated.

Longitudinal Transition Between Regular and Special Education in Autistic Children: Predictors and Policy Effects.

Inclusive education policies stimulate children with special educational needs, including autism, to attend regular education. We aimed to explore change over time in school placement and transitions of autistic children since the introduction of an inclusive education policy in the Netherlands (2014) and to examine the role of individual child characteristics. This study used longitudinal data from 2013 to 2021 on autistic children (N=1463, aged 5-16 years). We expected an increase in regular school placements and transitions to regular schools. Surprisingly, the proportion of children with autism in regular schools slightly decreased. Special school placement was more likely for boys and autistic children with lower intelligence scores, co-occurring conditions or behavioral and peer relation problems. Younger autistic children and those with lower intelligence scores more often transferred from a regular to a special school. The opposite transition was more common in older autistic children and those with higher intelligence scores. The likelihood of special-to-regular transitions did not change, but regular-to-special transitions were more prevalent during the years just after the policy change than in later years. The inclusive policy had little impact on the school placement, and at most led to a delayed drop in referrals to special schools.

Balancing Data Acquisition Benefits and Ordering Costs for Predictive Supplier Selection and Order Allocation

The strategic selection of suppliers and the allocation of orders across multiple periods have long been recognized as critical aspects influencing company expenditure and resilience. Leveraging the enhanced predictive capabilities afforded by machine learning models, direct lookahead models—linear programming models that optimize future decisions based on forecasts generated by external predictive modules—have emerged as viable alternatives to traditional deterministic and stochastic programming methodologies to solve related problems. However, despite these advancements, approaches implementing direct lookahead models typically lack mechanisms for updating forecasts over time. Yet, in practice, suppliers often exhibit dynamic behaviours, and failing to update forecasts can lead to suboptimal decision-making. This study introduces a novel approach based on parametrized direct lookahead models to address this gap. The approach explicitly addresses the hidden trade-offs associated with incorporating forecast updates. Recognizing that forecasts can only be updated by acquiring new data and that the primary means of acquiring supplier-related data is through order allocation, this study investigates the trade-offs between data acquisition benefits and order allocation costs. An experimental design utilizing real-world automotive sector data is employed to assess the potential of the proposed approach against various benchmarks. These benchmarks include decision scenarios representing perfect foresight, no data acquisition benefits, and consistently positive benefits. Empirical findings demonstrate that the proposed approach achieves performance levels comparable to those of decision-makers with perfect foresight while consistently outperforming benchmarks not balancing order allocation costs and data acquisition benefits.

Machine Learning in Transportation

The number of vehicles and people on the planet is growing, and this has made traffic management more complex and requires accuracy and efficiency that go beyond human intervention. This work explores the use of machine learning (ML) algorithms in intelligent transportation systems, with a focus on traffic control. It looks at how ML might help with issues caused by increasing traffic, such as accidents and congestion. In an effort to lessen traffic- related problems, the suggested ML-based Traffic Management System (TMS) keeps an eye on cars, adjusts traffic lights, finds congestion, and provides other routes. In order to construct an intelligent transport system that uses machine learning (ML) algorithms, stimulate efficient traffic data management, and improve contemporary traffic management systems, the article outlines the principles of operation, obstacles encountered, and suggestions offered.

Relative uniqueness or uniqueness? A new cross-efficiency evaluation approach with weight priority strategy

Due to a large number of studies on cross-evaluation strategies, the theoretical problem of DEA cross-efficiency approach has been transformed from the non-uniqueness of the optimal solution to the relative uniqueness, because the existing cross-evaluation strategies can only reduce the possibility of non-unique solutions, but cannot prove the existence of a unique optimal solution. This paper illustrates the relative uniqueness of optimal solution of the cross-efficiency approach with traditional cross-evaluation strategies, and the graphic method and theoretical analysis are used to explain the reasons of relative uniqueness. Based on the data characteristic of inputs and outputs, the weight priority rule is made for constructing the new cross-evaluation strategy, and its optimal solution is proved to be unique. In addition, the weight priority strategy can also be introduced into traditional strategies as a supplementary strategy, and then the unique optimal solution can be obtained on the premise of preserving the characteristics of traditional strategies. In general, this paper solves the non-uniqueness and relative uniqueness problem of optimal solution in the DEA cross-efficiency approach, and then the reliability and robustness of the approach are thus improved.

Algorithmic Decision Making: Can Artificial Intelligence and the Metaverse Provide Technological Solutions to Modernise the United Kingdom’s Legal Services and Criminal Justice?

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) have had a profound impact on various sectors including Banking (Fin Tech), Health (HealthTech) and Charitable Fundraising (Charity Tech). The ‘natural’ ability of an AI system to independently perform and, often, outthink its human-counter parts by developing ‘intelligence’(simulating human intelligence) through its own experiences and processing deep layers of information i.e., complex representations of data, and learn has resulted in astounding improvements in the completion of tasks that are complex and technical, time-consuming.AI, with the ease of working with the most granular level of detail, can identify people and objects, recognise voices, uncover patterns and, in advance, screen for problems. Yet, RegTech (or LawTech/LegalTech) has not seen the same level of advancement. AI can provide solutions and enormous economic, political, and social benefits – in terms of public service administration. The purpose of this article is to explore advents in AI (ML and DL) and whether the criminal justice system, in the United Kingdom (UK), which is heavily overburdened, could benefit from some of the advances that have taken place in other sectors and jurisdictions, and whether automation and algorithmic decision making could be used to modernise it. This research draws on domestic and international published law, regulation, and literature, and isset out in six parts, the first partre views the position of the criminal justice system i.e., issues, part two then looks at relative technological advancements in AI, and the Metaverse. Part three explores current advents in AI relating to RegTech (LawTech/LegalTech) and how, if at all, the CJS can use this technology. Part four explores what aspects of the U.K.’s CJS would be fit for automation. Part five focuses on those matters pertaining to AI that pose problems in relation to matters in part 4 i.e., AI discrimination and bias, and explores safeguarding and mitigation including the requirement for explanation as set out in the GDPR. Part six concludes the discussion with some recommendations, as at, January 2024. It is suggested that AI and algorithmic decision making, with the correct legal framework and safeguards in place, could assist in modernising the CJS focussed legal functions, services in law firms, innovating for the next decade. This work is original and timely given the increased debate relating to how AI can assist in modernising the U.K.’s CJS, the global criminal justice challenges, solutions, and what, if any, role the Metaverse can play.

An IoT system based on open APIs and geolocation for the prevention of human health disorders

The article presents a study devoted to improving the developed Internet of Things system based on open APIs and geolocation, which is aimed at analyzing data about the state of the environment using an expert approach and data visualization for possible prevention of human health disorders. Based on the developed Internet of Things system, open APIs, geolocation using intelligent gadgets, and the Meteorological Geographic Information System, the study generates a message about the danger to human health associated with meteorological factors. Accordingly, a person is informed promptly about potential risks and threats, particularly about the presence of pollen in the air, indicating the level of its concentration in the air, and about problems with air quality. What is the “anthropo-geo-sensory-digital” prerequisite for making effective real-time decisions to prevent human health disorders?New features were added to the developed system to analyze data about potential risks and threats that could lead to human health disorders, in particular, about the presence of temperature problems, under the condition that this indicator goes beyond the normative and optimal zone; the presence of relative humidity problems, under the condition that this indicator go beyond the normative and optimal zone; the presence of wind speed problems, if the air wind speed exceeds the permissible standards. Effective decision-making based on providing timely information about potential risks and threats to human health, in addition to preventive, has significant methodological and technological potential that can be used to improve the effectiveness of health care, both in extreme conditions and in conditions of sustainable existence. The system developed and improved by us can also be considered as one of the ways of introducing innovations in health care, the IT field, the educational process in institutions of higher education and conducting further research in this field, in particular, in the direction of data processing in health care systems based on machine learning.

To study the socio-demographic profile and clinical profile of abnormal uterine bleeding cases attending new civil hospital, Surat, Western India

There are so many concerns and worries about menstruation. With the use of advance method of contraception, women are experiencing more menstrual related problems. Now a days, menstrual related complaints are more common in gynaec clinic. So, we had conducted this study to document the sociodemographic and clinical profile among the attendees of gynaec clinic with menstrual related problem. Observational cross-sectional study carried out in western part of India with 100 patients after their written informed consent. Data were collected on pre-designed semi structured questionnaire which consist of sociodemographic and clinical questions. All data were entered in MS excel spreadsheet and analyzed with the help of SPSS V.20. Current study reported 8% of new Gynaec attendees had problem of abnormal uterine bleeding. Majority of patients were belonging to age group 21 years to 40 years (83%) and most of the patients were multipara (75%). Commonest complains among patients were menorrhagia (80%). Most of the patients were reported haemoglobin of less than 12 mg/dl.: Abnormal uterine bleeding is a distressing problem for women in their reproductive age groups especially in 21 years to 40 years.

WELL-POSEDNESS OF A NONLINEAR INTERFACE PROBLEM DESCRIBED BY HEMIVARIATIONAL INEQUALITIES VIA BOUNDARY INTEGRAL OPERATORS

AbstractThis paper is devoted to the well-posedness of a novel nonlinear interface problem on an unbounded domain with nonmonotone set-valued transmission conditions. This interface problem involves a nonlinear monotone partial differential equation in the interior domain and the Laplacian in the exterior domain. Such a scalar interface problem models nonmonotone frictional contact of elastic infinite media. The variational formulation of the interface problem leads to a hemivariational inequality (HVI), which however lives on the unbounded domain, and thus cannot be analyzed in a reflexive Banach space setting. Boundary integral methods lead to another HVI that is amenable to functional analytic methods using standard Sobolev spaces on the interior domain and Sobolev spaces of fractional order on the coupling boundary. Broadening the scope of the paper, we consider extended real-valued HVIs augmented by convex extended real-valued functions. Under a smallness hypothesis, we provide existence and uniqueness results and, moreover, establish a stability result for extended real-valued HVIs with respect to the extended real-valued function as a parameter. Based on the latter general stability result, we provide various stability results for the interface problem, as well as the stability of a related bilateral obstacle interface problem with respect to the obstacles.

Magic in generalized Rokhsar-Kivelson wavefunctions

Magic is a property of a quantum state that characterizes its deviation from a stabilizer state, serving as a useful resource for achieving universal quantum computation e.g., within schemes that use Clifford operations. In this work, we study magic, as quantified by the stabilizer Renyi entropy, in a class of models known as generalized Rokhsar-Kivelson systems, i.e., Hamiltonians that allow a stochastic matrix form (SMF) decomposition. The ground state wavefunctions of these systems can be written explicitly throughout their phase diagram, and their properties can be related to associated classical statistical mechanics problems, thereby allowing powerful analytical and numerical approaches that are not usually available in conventional quantum many body settings. As a result, we are able to express the SRE in terms of wave function coefficients that can be understood as a free energy difference of related classical problems. We apply this insight to a range of quantum many body SMF Hamiltonians, which affords us to study numerically the SRE of large high-dimensional systems, and in some cases to obtain analytical results. We observe that the behaviour of the SRE is relatively featureless across quantum phase transitions in these systems, although it is indeed singular (in its first or higher order derivative, depending on the nature of the transition). On the contrary, we find that the maximum of the SRE generically occurs at a cusp away from the quantum critical point, where the derivative suddenly changes sign. Furthermore, we compare the SRE and the logarithm of overlaps with specific stabilizer states, asymptotically realised in the ground state phase diagrams of these systems. We find that they display strikingly similar behaviors, which in turn establish rigorous bounds on the min-relative entropy of magic.

Is sleep bruxism in obstructive sleep apnea only an oral health related problem?

BackgroundThe etiology of sleep bruxism in obstructive sleep apnea (OSA) patients is not yet fully clarified. This prospective clinical study aimed to investigate the connection between probable sleep bruxism, electromyographic muscle tone, and respiratory sleep patterns recorded during polysomnography.Methods106 patients with OSA (74 males, 31 females, mean age: 56.1 ± 11.4 years) were divided into two groups (sleep bruxism: SB; no sleep bruxism: NSB). Probable SB were based on the AASM criteria: self-report of clenching/grinding, orofacial symptoms upon awakening, abnormal tooth wear and hypertrophy of the masseter muscle. Both groups underwent clinical examination for painful muscle symptoms aligned with Temporomandibular Disorders Diagnostic Criteria (DC/TMD), such as myalgia, myofascial pain, and headache attributed to temporomandibular disorder. Additionally, non-complaint positive muscle palpation and orofacial-related limitations (Jaw Functional Limited Scale-20: JFLS-20) were assessed. A one-night polysomnography with electromyographic masseter muscle tone (EMG) measurement was performed. Descriptive data, inter-group comparisons and multivariate logistic regression were calculated.ResultsOSA patients had a 37.1% prevalence of SB. EMG muscle tone (N1-N3, REM; P = 0.001) and the number of hypopneas (P = 0.042) were significantly higher in the sleep bruxism group. While measures like apnea–hypopnea-index (AHI), respiratory-disturbance-index (RDI), apnea index (AI), hypopnea-index (HI), number of arousals, and heart rate (1/min) were elevated in sleep bruxers, the differences were not statistically significant. There was no difference in sleep efficiency (SE; P = 0.403). Non-complaint masseter muscle palpation (61.5%; P = 0.015) and myalgia (41%; P = 0.010) were significant higher in SB patients. Multivariate logistic regression showed a significant contribution of EMG muscle tone and JFLS-20 to bruxism risk.ConclusionIncreased EMG muscle tone and orofacial limitations can predict sleep bruxism in OSA patients. Besides, SB patients suffer more from sleep disorder breathing. Thus, sleep bruxism seems to be not only an oral health related problem in obstructive apnea. Consequently, interdisciplinary interventions are crucial for effectively treating these patients.Trial registrationThe study was approved by the Ethics Committee of Philipps-University Marburg (reg. no. 13/22—2022) and registered at the “German Clinical Trial Register, DRKS” (DRKS0002959).

Flexural–torsional buckling with enforced axis of twist and related problems

The classical lateral–torsional instability of a cantilever beam with continuous elastic lateral restraint and a transverse point-load applied at the free end is discussed here through the lens of asymptotic simplifications. One of our main goals is to provide analytical approximations for the critical buckling load, as well as its dependence on various key non-dimensional groups. The first part of this study is concerned with a scenario in which a doubly symmetric beam is constrained to rotate about a fixed axis situated at an arbitrary height above the shear centroidal axis. The second part examines a beam that features a continuous elastic lateral restraint spanning its entire length. Assuming that the stiffness of the constraint, [Formula: see text] (say), is finite, the buckling equations in the second case are described by a system of two coupled fourth-order differential equations in the lateral displacement and the angle of twist. We show that as [Formula: see text], the problem discussed in the first part provides an outer solution for the aforementioned system; the relevant boundary conditions for the next-to-leading-order outer approximation are also derived using matched asymptotics. Our theoretical findings are confirmed by comparisons with direct numerical simulations of the full buckling problem.

Spatio-temporal dynamics of agricultural and built-up areas in the Niaye area of Pikine urban wetland (Dakar, Senegal)

The Niaye area of Pikine, a wetland with favorable physical and climatic conditions, is a rich and varied ecosystem that guarantees a supply of water and fertile soil, conducive to market gardening. This plays an important socio-economic role in the Dakar region. However, this sensitive and fragile ecosystem is often affected by poor farming practices. The aim of the study was to analyze the spatial evolution of agricultural areas and built-up areas in order to identify related environmental problems. Interview guides, aerial photographs from 1966 and 1978 and Digital Globe Over view 2 satellite images from 2004 and 2022 were used. The interview guides were used to obtain data on the management of the market gardening activity, and also to identify the environmental problems it generates. Satellite data processing revealed, through cartographic analysis, a progressive market gardening dynamic of +5.07%. In addition, the built-up area around the lake has increased by over 200%, resulting in a 68.4% decline in upland vegetation and a migration of market gardeners to the lake. This situation leads to the destruction of market garden crops in the rainy season as the water rises. What's more, the intensification of market gardening in response to the population's growing demand for vegetables has not failed to encourage bad practices, including the use of pesticides and sewage, which is a public health problem and a threat to the future of this wetland.

Hospital Acquired Infection Revisited: Route of Transmission

Aims: to revisited hospital route of infection transmission in patients under medical care with its related problems and re-focus on some important MDR nosocomial infection agents route of transmission to vulnerable patients. Discussion: Accidental infections acquired in hospitals or hospital acquired infection (HAI) is a global major health condition, and become a major obstacle in the present day era of hospitalization. Increased risk of morbidity with mortality, lengthened hospitalization, massive prescription of antibiotics, and socio-economically become a definite financial burden. Disinfection and sterilization in hospitals is of increasing concern. Besides that, the abuse of antibiotics has led to the prompt wide spread of multidrug-resistant drugs (MDR) microorganisms which is characterized by the ability to quickly adapt to an environment that previously does not support its growth commonly and able to initiate serious life-threatening HAI. The need to understand route of transmission and its associated obstacles will help combating MDR microorganisms. Conclusion: The Global emergence of MDR microorganisms with its difficult route of infections has created a major obstacle to combat the bacterial pathogens, and need serious attention from all stakeholders, throughout the world.

A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems.

Evolutionary Computation (EC) often throws away learned knowledge as it is reset for each new problem addressed. Conversely, humans can learn from small-scale problems, retain this knowledge (plus functionality) and then successfully reuse them in larger-scale and/or related problems. Linking solutions to problems together has been achieved through layered learning, where an experimenter sets a series of simpler related problems to solve a more complex task. Recent works on Learning Classifier Systems (LCSs) has shown that knowledge reuse through the adoption of Code Fragments, GP-like tree-based programs, is plausible. However, random reuse is inefficient. Thus, the research question is how LCS can adopt a layered-learning framework, such that increasingly complex problems can be solved efficiently? An LCS (named XCSCF*) has been developed to include the required base axioms necessary for learning, refined methods for transfer learning and learning recast as a decomposition into a series of subordinate problems. These subordinate problems can be set as a curriculum by a teacher, but this does not mean that an agent can learn from it. Especially if it only extracts over-fitted knowledge of each problem rather than the underlying scalable patterns and functions. Results show that from a conventional tabula rasa, with only a vague notion of what subordinate problems might be relevant, XCSCF* captures the general logic behind the tested domains and therefore can solve any n-bit Multiplexer, n-bit Carry-one, n-bit Majority-on, and n-bit Even-parity problems. This work demonstrates a step towards continual learning as learned knowledge is effectively reused in subsequent problems.

Error estimates for a mixed finite element method for the Maxwell’s transmission eigenvalue problem

In this paper, we analyze a numerical method combining the Ciarlet-Raviart mixed finite element formulation and an iterative algorithm for the Maxwell’s transmission eigenvalue problem. The eigenvalue problem is first written as a nonlinear quad-curl eigenvalue problem. Then the real transmission eigenvalues are proved to be the roots of a non-linear function. They are the generalized eigenvalues of a related linear self-adjoint quad-curl eigenvalue problem. These generalized eigenvalues are computed by a mixed finite element method. We derive the error estimates using the spectral approximation of compact operators, the theory of mixed finite element method for quad-curl problems, and the derivatives of eigenvalues.

An experimental and numerical investigation into the sensitivity of Rayleigh–Bénard convection to heat loss through the sidewalls

Thermal buoyancy flows and related heat transfer problems play a crucial role in a variety of fields and industrial applications. The present study is structured around the main objective of systematically investigating the response of fluid convection of the Rayleigh–Bénard type to heat loss taking place through the lateral walls of the considered fluid container. We elucidate, both experimentally and numerically, new aspects which deeply affect the dynamics when the classical assumptions of adiabatic or conducting walls cease to be valid. The emerging spatio-temporal behaviors are examined with respect to several parameters or conditions, including the dimensionality of the system (made accessible by dedicated numerical simulations based on the time-dependent and non-linear governing equations), the average temperature of the liquid, the applied temperature difference, the dependence of fluid properties on temperature and the intensity of heat transfer to the ambient. The results reveal the interesting triadic relationship among the heat exchanged through non-thermally controlled walls, the hierarchy of bifurcations displayed by the flow and the related multiplicity of solutions.

A systematic comparison of deep learning methods for Gleason grading and scoring

Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available.

A survey on the implementation of clinical medication reviews in community pharmacies within a multidisciplinary setting

BackgroundPolypharmacy is common in chronic medication users, which increases the risk of drug related problems. A suitable intervention is the clinical medication review (CMR) that was introduced in the Netherlands in 2012, but the effectiveness might be hindered by limited implementation in community pharmacies. Therefore our aim was to describe the current implementation of CMRs in Dutch community pharmacies and to identify barriers to the implementation.MethodsAn online questionnaire was developed based on the Consolidated Framework for Implementation Research (CFIR) and consisted of 58 questions with open ended, multiple choice or Likert-scale answering options. It was sent out to all Dutch community pharmacies (n = 1,953) in January 2021. Descriptive statistics were used.ResultsA total of 289 (14.8%) community pharmacies filled out the questionnaire. Most of the pharmacists agreed that a CMR has a positive effect on the quality of pharmacotherapy (91.3%) and on medication adherence (64.3%). Pharmacists structured CMRs according to available selection criteria or guidelines (92%). Pharmacists (90%) believed that jointly conducting a CMR with a general practitioner (GP) improved their mutual relationship, whereas 21% believed it improved the relationship with a medical specialist. Lack of time was reported by 43% of pharmacists and 80% (fully) agreed conducting CMRs with a medical specialist was complicated. Most pharmacists indicated that pharmacy technicians can assist in performing CMRs, but they rarely do in practice.ConclusionsLack of time and suboptimal collaboration with medical specialists are the most important barriers to the implementation of CMRs.