Patient satisfaction and follow-up adherence to glaucoma case management clinic in China

AIM:To assess glaucoma patient satisfaction and follow-up adherence in case management and identify associated predictors to improve healthcare quality and patient outcomes.METHODS:In this cross-sectional study,a total of 119 patients completed a Patient Satisfaction Questionnaire-18 and a sociodemographic questionnaire.Clinical data was obtained from the case management system.Follow-up adherence was defined as completing each follow-up within±30d of the scheduled time set by ophthalmologists during the study period.RESULTS:Average satisfaction scored 78.65±7,with an average of 4.39±0.58 across the seven dimensions.Age negatively correlated with satisfaction(P=0.008),whilst patients with follow-up duration of 2 or more years reported higher satisfaction(P=0.045).Multivariate logistics regression analysis revealed that longer follow-up durations were associated with lower follow-up adherence(OR=0.97,95%CI,0.95-1.00,P=0.044).Additionally,patients with suspected glaucoma(OR=2.72,95%CI,1.03-7.20,P=0.044)and those with an annual income over 100000 Chinese yuan demonstrated higher adherence(OR=5.57,95%CI,1.00-30.89,P=0.049).CONCLUSION:The case management model proves effective for glaucoma patients,with positive adherence rates.The implementation of this model can be optimized in the future based on the identified factors and extended to glaucoma patients in more hospitals.

Special Issue:Questions&Data for Better Science and Innovation Call for submissions

Editors Yang Wang,Xi'an Jiaotong University Dongbo Shi,Shanghai Jiaotong University Ye Sun,University College London Zhesi Shen,National Science Library,CAS Topic of the Special Issue What are the top questions towards better science and innovation and the required data to answer these questions?

Loss to Specialized Cardiology Follow-Up in Adults Living with Congenital Heart Disease

Background:Much has been written about the loss to follow-up in the transition between pediatric and adult Congenital Heart Disease(CHD)care centers.Much less is understood about the loss to follow-up(LTF)after a successful transition.This is critical too,as patients lost to specialised care are more likely to experience mor-bidity and premature mortality.Aims:To understand the prevalence and reasons for loss to follow-up(LTF)at a large Australian Adult Congenital Heart Disease(ACHD)centre.Methods:Patients with moderate or highly complex CHD and gaps in care of>3 years(defined as LTF)were identified from a comprehensive ACHD data-base.Structured telephone interviews examined current care and barriers to clinic attendance.Results:Overall,407(22%)of ACHD patients(n=1842)were LTF.The mean age at LTF was 31(SD 11.5)years and 54%were male;311(76%)were uncontactable.Compared to adults seen regularly,lost patients were younger,with a greater socio-economic disadvantage,and had less complex CHD(p<0.05 for all).We interviewed 59 patients(14%).The top 3 responses for care absences were“feeling well”(61%),losing track of time(36%),and not needing fol-low-up care(25%).Conclusions:A large proportion of the ACHD population becomes lost to specialised cardiac care,even after a successful transition.This Australian study reports younger age,moderate complexity defects,and socio-economic disadvantage as predictive of loss to follow-up.This study highlights the need for novel approaches to patient-centered service delivery even beyond the age of transition and resources to maintain patient engagement within the ACHD service.

Long-term assessment of collagenase treatment for Dupuytren’s contracture:A 10-year follow-up study

BACKGROUND Enzymatic fasciotomy with collagenase clostridium histolyticum(CCH)has revolutionized the treatment for Dupuytren’s contracture(DC).Despite its benefits,the long-term outcomes remain unclear.This study presented a comprehensive 10-year follow-up assessment of the enduring effects of CCH on patients with DC.AIM To compare the short-term(12 wk)and long-term(10 years)outcomes on CCH treatment in patients with DC.METHODS A cohort of 45 patients was treated with CCH at the metacarpophalangeal(MCP)joint and the proximal interphalangeal(PIP)joint and underwent systematic reevaluation.The study adhered to multicenter trial protocols,and assessments were conducted at 12 wk,7 years,and 10 years post-surgery.RESULTS Thirty-seven patients completed the 10-year follow-up.At 10 years,patients treated at the PIP joint exhibited a 100%recurrence.However,patients treated at the MCP joint only showed a 50%recurrence.Patient satisfaction varied,with a lower satisfaction reported in PIP joint cases.Recurrence exceeding 20 degrees on the total passive extension deficit was observed,indicating a challenge for sustained efficacy.Significant differences were noted between outcomes at the 7-year and 10-year intervals.CONCLUSION CCH demonstrated sustained efficacy when applied to the MCP joint.However,caution is warranted for CCH treatment at the PIP joint due to a high level of recurrence and low patient satisfaction.Re-intervention is needed within a decade of treatment.

PAL-BERT:An Improved Question Answering Model

In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has surfaced.Consequently,model training has become more costly and less efficient.To enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language model.Firstly,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is formed.Then,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of ReLU to improve the performance.Finally,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training efficiency.Compared with traditional models,PAL-BERT significantly improves the NLP task’s performance.

Environmental Impact Assessment Follow-Up of Seismic Survey Offshore Activities in Brazil

Follow-up of environmental impacts is an integral part of the Environmental Impact Assessment (EIA) process, closely related to the effectiveness of the instrument. EIA follow-up has been receiving a lot of interest from scientists and practitioners, though it is recognized as one of the weakest points of EIA systems globally. Also, EIA follow-up is influenced by the context, mainly in terms of the types of projects or activities and their related impacts on the environment. Therefore, the present paper is focused on the investigation of the follow-up stage applied to the activity of seismic survey coupled with offshore oil & gas exploitation in Brazil. Research was based on a qualitative approach that included document analysis and semi-structured interviews with analysts involved in EIA processes, and sought to generate evidence of effectiveness of the EIA follow-up as conducted by the Federal Environment Agency (Ibama) in order to situate the practice of follow-up in the broader context of international best practice principles. Based on the findings, it was concluded that, due to the peculiarities of offshore seismic survey, it is necessary to promote adaptations in the procedures for monitoring impacts in order to ensure proper alignment with the principles and conceptual foundations that guide EIA practice. Specifically, the timing of the execution of the activity imposes challenges for its integration into the “conventional” cycle that has guided the monitoring of the impacts in the EIA of projects.

Operational requirements analysis method based on question answering of WEKG

The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

Questioning Styles in Vocational College English Classrooms

This study investigates the effects of TBLT reform in Higher Vocational Colleges from the perspective of questioning styles.It employs three methods to collect data:classroom observation,semi-structured interviews and focus group discussion with eight English teachers and their 384 non-English major students from three Higher Vocational Colleges in Guangdong.The results indicated that the teachers assigned students different tasks to perform in class.They seemed to be adopting the TBLT approach,but their English classes were not totally different from the teacher-centered grammar-focused lessons,the student-centered or communicative lessons.

A Study on Teacher's Questioning Strategies in the UK

Classroom questioning is one of the main means for classroom interaction which plays a very important role in classroom teaching. Therefore, based on the observation of four different level English classes in the UK and interview of English teachers, this thesis investigates the types, functions and answer-seeking strategies used by EFL teachers.

An Investigation of Teacher Questioning Strategies Usage in College English Course——A Case Study on Honghe University

Teacher questioning strategy is one of popular teaching method in teaching practice.The teachers and students of college English course II was taken as investigate objects in this study.This study will focus on the problems of questioning frequency,questioning types,questioning strategies,waiting time and students’response to deeply study this situation.Through the data collection and analysis,the researcher hopes the study will deeply reveal the real situation of teacher questioning usage in Honghe University.Some referential suggestions will be offered to the college English teachers for their future teaching.

Application of Questioning Strategy in English Reading Teaching

This paper mainly deals with the application of questioning strategy in English reading teaching.As one of the most useful methods,teachers usually attach great importance to questioning,which is proved to be very effective in teaching.

The Role of Questioning in the Teaching of Reading

In learner-centered classroom,teachers' questioning serves as very important stimuli to foster students' participation in classroom interaction.A classroom full of active interactions is an ideal environment for foreign language acquisition(FLA).Questioning is a critical strategy for the instructors to engage students in reading activities so as to help them build the habit of independent learning.

Teachers’Questioning Strategies in Reading Class—the Perspective of Adaptation

Teachers’questioning is one of the crucial means for classroom interaction.This study attempts to explore teachers’questioning strategies in reading class from the perspective of Adaptation Theory.It is found that the effective questioning can be realized in teachers’adaptation to their roles,the features of students and the features of reading courses.

A Self-reflection on the Questioning in the English Class in Vocational College

Questioning is a indispensible part of classroom teaching and a measurement of classroom performance in our vocational college. But there is not enough importance had attached on it in the author's class. In this essay,the author researched on previous theories and peer studies on questioning,in accordance on the specific situation of our college,trying to figure out how to improve the questioning behavior in the class.

Brief Introduction about Questioning Art in English Classroom

here is no doubt that teacher’s language has deep effects on the EFL classroom language teaching.To make a further explanation about how and why important teacher’s language is,this paper intends to make more clear elucidation about its strategies according to the following specific parts.

Questioning Practice in Oral Teaching for Higher Vocational Students

In order to improve the students’ language competence,the author paid much attention to the students’ questioning practice.It,to some extent,turns out to be an effective method to train the students to use appropriate language grammatically and sociallinguistically.

Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic interest.Scholars have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs engender.To clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature review.The goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)users.The final review included 133 articles.Dominant research themes include question quality,answer quality,and expert identification.In terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack Overflow.The scope of most articles was confined to just one platform with few cross-platform investigations.Articles with ML outnumber those with DL.Nonetheless,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

Recently,pre-trained language representation models such as bidirec-tional encoder representations from transformers(BERT)have been performing well in commonsense question answering(CSQA).However,there is a problem that the models do not directly use explicit information of knowledge sources existing outside.To augment this,additional methods such as knowledge-aware graph network(KagNet)and multi-hop graph relation network(MHGRN)have been proposed.In this study,we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers(ALBERT)with knowledge graph information extraction technique.We also propose to applying the novel method,schema graph expansion to recent language models.Then,we analyze the effect of applying knowledge graph-based knowledge extraction techniques to recent pre-trained language models and confirm that schema graph expansion is effective in some extent.Furthermore,we show that our proposed model can achieve better performance than existing KagNet and MHGRN models in CommonsenseQA dataset.

Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web services.How to model questions and users in the heterogeneous content network is critical to this task.Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues.Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling.However,they often fail to distinguish the nodes’personalized preferences and only consider the textual content of a part of the nodes in network embedding learning,while ignoring the semantic relevance of nodes.In this paper,we propose a novel framework that jointly considers the structural proximity relations and textual semantic relevance to model users and questions more comprehensively.Specifically,we learn topology-based embeddings through a hierarchical attentive network learning strategy,in which the proximity information and the personalized preference of nodes are encoded and preserved.Meanwhile,we utilize the node’s textual content and the text correlation between adjacent nodes to build the content-based embedding through a meta-context-aware skip-gram model.In addition,the user’s relative answer quality is incorporated to promote the ranking performance.Experimental results show that our proposed framework consistently and significantly outperforms the state-of-the-art baselines on three real-world datasets by taking the deep semantic understanding and structural feature learning together.The performance of the proposed work is analyzed in terms of MRR,P@K,and MAP and is proven to be more advanced than the existing methodologies.