Comparison of hepatitis C virus testing recommendations in high-income countries

AIM To investigate hepatitis C virus(HCV) testing recommendations from the United States and other high-income countries.METHODS A comprehensive search for current HCV testing recommendations from the top quartile of United Nations Human Development Index(HDI) countries(very high HDI) was performed using Google and reviewed from May 1-October 30, 2014 and re-reviewed April 1-October 2, 2017. RESULTS Of the 51 countries identified, 16 had HCV testing recom-mendations from a government body or recommendations issued collaboratively between a government and a medical organization. Of these 16 countries, 15 had HCV testing recommendations that were primarily risk-based and highlight behaviors, exposures, and conditions that are associated with HCV transmission in that region. In addition to risk-based testing, the HCV Guidance Panel(United States) incorporates recommendations for aone-time test for individuals born during 1945-1965(the birth cohort) without prior ascertainment of risk into their guidance. In addition to the United States, six other countries either have an age-based testing recommendation or recommend one-time testing for all adults independent of risk factors typical of the region. CONCLUSION This review affirmed the similarities of the HCV Guidance Panel's guidance with those of recommendations from very high HDI countries.

Efficacy of fexuprazan compared with rebamipide in Korean patients with gastritis:A matching-adjusted indirect comparison

BACKGROUND Gastritis is one of the most frequently diagnosed diseases requiring medical treatment in South Korea.Fexuprazan,a novel potassium-competitive acid blocker,has been approved for treating gastritis and erosive esophagitis.Meanwhile,rebamipide is the most commonly used mucoprotective agent for acute and chronic gastritis in real-world settings in South Korea.However,there have been no studies comparing the efficacy of these two drugs yet.AIM To compare the efficacy of fexuprazan with that of rebamipide for acute and chronic gastritis.METHODS This was a matching-adjusted indirect comparison.Individual patient data from a phase III study of fexuprazan(10 mg BID)were compared with cumulative data from two matching studies of rebamipide(100 mg TID).Erosion improvement and healing rates were compared between two weeks of fexurapan,two weeks of rebamipide,and four weeks of rebamipide.The two main outcome variables were presented as percentages,and the risk differences(RD)and 95%confidence intervals(CI)were calculated for the relative treatment effects.RESULTS In the primary analysis,the erosion improvement and healing rates after a twoweek treatment with fexuprazan were 64.5%and 53.2%,respectively,while a twoweek treatment with rebamipide resulted in erosion improvement and healing rates of 43.6%(RD:21.0%;95%CI:9.6-32.3;P<0.01)and 35.6%(RD:17.6%;95%CI:6.1-29.2;P=0.003),respectively.In the additional analysis,the erosion improvement and healing rates for the two-week fexuprazan treatment(64.2%and 51.2%,respectively)were similar to those obtained during a four-week treatment with rebamipide(60.6%;RD:3.6%;95%CI:-9.8,17.0;P=0.600 and 53.5%;RD:-2.3%;95%CI:-16.1,11.5;P=0.744,respectively).CONCLUSION The two-week fexuprazan treatment was superior to the two-week rebamipide treatment and similar to the fourweek rebamipide treatment for patients with gastritis.

Comparison of Electronic Taxpayer Services in OECD Countries and Recommendations for China

Through comparing development trend electronic taxpayer services in OECD countries and analyzing its drawbacks,it gained beneficial experience of electronic taxpayer services.Then,it came up with policy recommendations for China.It is recommended that China should raise the electronic taxpayer services to national strategy level,improve the efficiency of electronic taxpayer services in line with the taxpayercentered principle,develop ways of electronic taxpayer services with Chinese characteristics,increase convenience for taxpayers on the precondition of guaranteeing information security,make effort to reduce compliance costs of taxpayers,and promote popularization of electronic taxpayer services with the framework of laws.

A multilayer network diffusion-based model for reviewer recommendation

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

An Adaptive Program Recommendation System for Multi-User Sharing Environment

More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and they cannot make effective use of the mixed information generated by multi-user when exploring users’potential interests.To solve these problems,this paper proposes an adaptive program recommendation system for multi-user sharing environment.Specifically,we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions,which can separate the profile of target user from mixed log records.Subsequently,an online recommendation module with adaptive timevarying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module.On one hand,to learn the dynamic changes in user-interest,a time-varying linear upper confidence bound(LinUCB)based on personal information is designed.On the other hand,to reduce the risk of exploration,a timeinvariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user,which is integrated into the time-varying LinUCB by cross-weighting strategy.Finally,experimental results validate the efficiency of the proposed scheme.

Improving Diversity with Multi-Loss Adversarial Training in Personalized News Recommendation

Users’interests are often diverse and multi-grained,with their underlying intents even more so.Effectively captur-ing users’interests and uncovering the relationships between diverse interests are key to news recommendation.Meanwhile,diversity is an important metric for evaluating news recommendation algorithms,as users tend to reject excessive homogeneous information in their recommendation lists.However,recommendation models themselves lack diversity awareness,making it challenging to achieve a good balance between the accuracy and diversity of news recommendations.In this paper,we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity.Unlike most existing works that solely optimize accuracy or employ more features to meet diversity,the proposed algorithm leverages the diversity-aware capability of the model.First,we introduce an augmented user model to fully capture user intent and the behavioral guidance they might undergo as a result.Specifically,we focus on the relationship between the original clicked news and the augmented clicked news.Moreover,we propose an effective adversarial training method for diversity(AT4D),which is a pluggable component that can enhance both the accuracy and diversity of news recommendation results.Extensive experiments on real-world datasets confirm the efficacy of the proposed algorithm in improving both the accuracy and diversity of news recommendations.

Urban Traffic Control Meets Decision Recommendation System:A Survey and Perspective

Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems.Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and expertise.However,this process is cumbersome,labor-intensive,and cannot be applied on a large network scale.Recent studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and scalability.Such a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been conducted.In this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field.

Recommendation Method for Contrastive Enhancement of Neighborhood Information

Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.

The use of oral human immunodeficiency virus pre-exposure prophylaxis in pregnant and lactating women in sub-Saharan Africa:considerations,barriers,and recommendations

In sub-Saharan Africa(SSA),63%of new human immunodeficiency virus(HIV)infections in 2021 were among women,particularly adolescent girls,and young women.There is a high incidence of HIV among pregnant and lactating women(PLW)in SSA.It is estimated that the risk of HIV-acquisition during pregnancy and the postpartum period more than doubles.In this article,we discuss the safety and effectiveness of drugs used for oral HIV pre-exposure prophylaxis(PrEP),considerations for initiating PrEP in PLW,the barriers to initiating and adhering to PrEP among them and suggest recommendations to address these barriers.Tenofovir/emtricitabine,the most widely used combination in SSA,is safe,clinically effective,and cost-effective among PLW.Any PLW who requests PrEP and has no medical contraindications should receive it.PrEP users who are pregnant or lactating may experience barriers to starting and adhering for a variety of reasons,including personal,pill-related,and healthcare facility-related issues.To address the barriers,we recommend an increased provision of information on PrEP to the women and the communities,increasing and/or facilitating access to PrEP among the PLW,and developing strategies to increase adherence.

Recommendation System Based on Perceptron and Graph Convolution Network

The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data.This paper presents a new approach to address such issues,utilizing the graph convolution network to extract association relations.The proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction layer.The embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation layer.The forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph convolution.Furthermore,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction layer.The score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,respectively.Finally,the prediction score of users to items is obtained.The recall rate and normalized discounted cumulative gain were used as evaluation indexes.The proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms.

Multi-party semi-quantum private comparison protocol of size relation based on two-dimensional Bell states

Currently,all quantum private comparison protocols based on two-dimensional quantum states can only compare equality,via using high-dimensional quantum states that it is possible to compare the size relation in existing work.In addition,it is difficult to manipulate high-dimensional quantum states under the existing conditions of quantum information processing,leading to low practicality and engineering feasibility of protocols for comparing size relation.Considering this situation,we propose an innovative protocol.The proposed protocol can make size comparison by exploiting more manageable two-dimensional Bell states,which significantly enhances its feasibility with current quantum technologies.Simultaneously,the proposed protocol enables multiple participants to compare their privacies with the semi-quantum model.The communication process of the protocol is simulated on the IBM Quantum Experience platform to verify its effectiveness.Security analysis shows that the proposed protocol can withstand common attacks while preserving the privacies of all participants.Thus,the devised protocol may provide an important reference for implementation of quantum private size comparison protocols.

Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.

Diversified and compatible web APIs recommendation based on game theory in IoT

With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can be accessible remotely.In this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile applications.However,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs selected.Considering this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this paper.First of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation subgraphs.Afterwards,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search problem.At last,a set of experiments are designed and implemented on a real dataset crawled from www.programmableweb.com.Experimental results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.

Levels of evidence and grades of recommendation supporting European society for medical oncology clinical practice guidelines

Background:The European Society for Medical Oncology(ESMO)guidelines are among the most comprehensive and widely used clinical practice guidelines(CPGs)globally.However,the level of scientific evidence supporting ESMO CPG recommendations has not been systematically investigated.This study assessed ESMO CPG levels of evidence(LOE)and grades of recommendations(GOR),as well as their trends over time across various cancer settings.Methods:We manually extracted every recommendation with the Infectious Diseases Society of America(IDSA)classification from each CPG.We examined the distribution of LOE and GOR in all available ESMO CPG guidelines across different topics and cancer types.Results:Among the 1,823 recommendations in the current CPG,30%were classified as LOEⅠ,and 43%were classified as GOR A.Overall,there was a slight decrease in LOEⅠ(−2%)and an increase in the proportion of GOR A(+1%)in the current CPG compared to previous versions.The proportion of GOR A recommendations based on higher levels of evidence such as randomized trials(LOEⅠ–Ⅱ)shows a decrease(71%vs.63%,p=0.009)while recommendations based on lower levels of evidence(LOEⅢ–Ⅴ)show an increase(29%vs.37%,p=0.01)between previous and current version.In the current versions,the highest proportion of LOEⅠ(42%)was found in recommendations related to pharmacotherapy,while the highest proportion of GOR A recommendations was found in the areas of pathology(50%)and diagnostic(50%)recommendations.Significant variability in LOEⅠand GOR A recommendations and their changes over time was observed across different cancer types.Conclusion:One-third of the current ESMO CPG recommendations are supported by the highest level of evidence.More well-designed randomized clinical trials are needed to increase the proportion of LOEⅠand GOR A recommendations,ultimately leading to improved outcomes for cancer patients.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

Efficacy comparison of multipoint and single point scanning panretinal laser photocoagulation in non-proliferative diabetic retinopathy treatment

BACKGROUND Non-proliferative diabetic retinopathy(NPDR)poses a significant challenge in diabetes management due to its microvascular changes in the retina.Laser photocoagulation,a conventional therapy,aims to mitigate the risk of progressing to proliferative diabetic retinopathy(PDR).AIM To compare the efficacy and safety of multi-spot vs single-spot scanning panretinal laser photocoagulation in NPDR patients.METHODS Forty-nine NPDR patients(86 eyes)treated between September 2020 and July 2022 were included.They were randomly allocated into single-spot(n=23,40 eyes)and multi-spot(n=26,46 eyes)groups.Treatment outcomes,including bestcorrected visual acuity(BCVA),central macular thickness(CMT),and mean threshold sensitivity,were assessed at predetermined intervals over 12 months.Adverse reactions were also recorded.RESULTS Energy levels did not significantly differ between groups(P>0.05),but the multi-spot group exhibited lower energy density(P<0.05).BCVA and CMT improvements were noted in the multi-spot group at one-month posttreatment(P<0.05).Adverse reaction incidence was similar between groups(P>0.05).CONCLUSION While energy intensity and safety were comparable between modalities,multi-spot scanning demonstrated lower energy density and showed superior short-term improvements in BCVA and CMT for NPDR patients,with reduced laser-induced damage.

Video Recommendation System Using Machine-Learning Techniques

In the realm of contemporary artificial intelligence,machine learning enables automation,allowing systems to naturally acquire and enhance their capabilities through learning.In this cycle,Video recommendation is finished by utilizing machine learning strategies.A suggestion framework is an interaction of data sifting framework,which is utilized to foresee the“rating”or“inclination”given by the different clients.The expectation depends on past evaluations,history,interest,IMDB rating,and so on.This can be carried out by utilizing collective and substance-based separating approaches which utilize the data given by the different clients,examine them,and afterward suggest the video that suits the client at that specific time.The required datasets for the video are taken from Grouplens.This recommender framework is executed by utilizing Python Programming Language.For building this video recommender framework,two calculations are utilized,for example,K-implies Clustering and KNN grouping.K-implies is one of the unaided AI calculations and the fundamental goal is to bunch comparable sort of information focuses together and discover the examples.For that K-implies searches for a steady‘k'of bunches in a dataset.A group is an assortment of information focuses collected due to specific similitudes.K-Nearest Neighbor is an administered learning calculation utilized for characterization,with the given information;KNN can group new information by examination of the‘k'number of the closest information focuses.The last qualities acquired are through bunching qualities and root mean squared mistake,by using this algorithm we can recommend videos more appropriately based on user previous records and ratings.

Global Research Progress on Municipal Waste and Future Prospect Based on the Cross-national Comparisons

Due to the acceleration of urbanization,the municipal waste(MW)problem has transformed into a global challenge for urb-an sustainability.To elucidate historical trends,current focal points,and future directions in MW research,we conducted a bibliometric analysis and employed knowledge graph visualization to scrutinize a total of 34212 articles,which were published between 1991 and 2021 in the Web of Science(WoS)core database.The results indicated that current major research themes encompass waste classifica-tion and recycling,waste management and public behavior,waste disposal methods and technologies,as well as environmental impact and evaluation.There has been a shift in the research focus from the environmental impacts of waste incineration to sustainable manage-ment related issues.A comparison of research from six typical countries revealed the differences in research priorities and techniques advantages.Scholars from the USA and Britain initiated MW research earlier than other countries and investigated management issues in depth,such as public behavior and willingness to pay.Meanwhile,Japanese,German,and Swedish scholars conducted extensive studies on advanced waste treatment technologies,such as disposal and recycling,risk assessment,and waste-to-energy techniques.Chinese scholars placed particular emphasis on end-of-pipe treatments and their associated environmental impacts.Hotspots and poten-tial future frontiers were identified by burst detection analysis.Keywords with high value of burst index(BI)worldwide are food waste and circular economy.Chinese scholars have put great efforts on waste environmental impact and its recycling technologies,while we’re expecting to further investigating vulnerable population.Furthermore,this study contributes to bridging the regional gap of scientific research among different countries and fostering international collaboration.

Chest Radiography: General Practitioners’ Compliance with Recommendations

Introduction: Chest radiography is the most frequently prescribed imaging test in general practice in France. We aimed to assess the extent to which general practitioners follow the recommendations of the French National Authority for Health in prescribing chest radiography. Methodology: We conducted a retrospective analysis study, in two radiology centers belonging to the same group in Saint-Omer and Aire-sur-la-Lys, of requests for chest radiography sent by general practitioners over the winter period between December 22, 2013, and March 21, 2014, for patients aged over 18 years. Results: One hundred and seventy-seven requests for chest X-rays were analyzed, 71.75% of which complied with recommendations. The most frequent reason was the search for bronchopulmonary infection, accounting for 70.08% of prescriptions, followed by 11.2% for requests to rule out pulmonary neoplasia, whereas the latter reason did not comply with recommendations. Chest X-rays contributed to a positive diagnosis in 28.81% of cases. The positive diagnosis was given by 36.22% of the recommended chest X-rays, versus 10% for those not recommended. Conclusion: In most cases, general practitioners follow HAS recommendations for prescribing chest X-rays. Non-recommended chest X-rays do not appear to make a major contribution to diagnosis or patient management, confirming the value of following the recommendations of the French National Authority for Health.