General Domain Research Articles

Abstract 4124606: Disparities in statin use following identification of Coronary Artery Calcium

Introduction: Coronary artery calcium (CAC) scoring is a useful tool for risk stratification in asymptomatic individuals, and guidelines recommend statin use in individuals with CAC. A growing body of research has aimed to identify and mitigate health disparities and their relation to CVD risk. Likewise, studies have highlighted social determinants of health (SDOH) that contribute to health disparities in CVD. We sought to extend this work by evaluating whether disparities exist with regards to statin use after identification of CAC within the Multi-Ethnic Study of Atherosclerosis (MESA). Methods: The sample consisted of all MESA participants with baseline CAC >0, not already on statin therapy, and without prior CVD events (n=2665). The association between race/ethnicity, age, sex, primary language, and an aggregate SDOH score with statin prescription at short (exam 2, median 1.6 year) and long (exam 5, median 9.4 year) term follow-up was evaluated in logistic regression models with adjustment for traditional CVD risk factors. The SDOH score was created from 14 components which covered the 5 general domains described in the Healthy People 2030 initiative (Figure 1). Results: Rates of statin prescription after CAC identification by race/ethnicity, language, and SDOH score tertile are shown in Figure 2. At short-term follow up, those with a higher SDOH score (worse burden) (OR 0.39, 95% CI 0.16-0.91), Hispanic / Latino participants (OR 0.59, 95% CI 0.40-0.85) and Spanish speaking participants (OR 0.51, 95% CI 0.30-0.83) were less likely to report statin use following CAC identification. At long-term follow up, Black participants (OR 0.71, 95% CI 0.52-0.96), Chinese participants (OR 0.58, 95% CI 0.39-0.86) and Chinese speaking participants (OR 0.50, 95% CI 0.33-0.76) were also less likely to report statin use following CAC identification, and a trend was noted for SDOH score (OR 0.53, 95% CI 0.26-1.09). Conclusion: This study identifies disparities in statin use after identification of CAC by race/ethnicity, language and social determinants of health. Future studies should investigate underlying reasons for these disparities and potential interventions to reduce disparities in cardiovascular care.

Abstract 4141457: Forms of Childhood Maltreatment and Subclinical Cardiovascular Disease Risk in Black men and women

Background: Exposure to specific forms of childhood maltreatment have been associated with cardiovascular disease (CVD) risk factors and CVD events in adulthood. Heterogenous experiences of childhood maltreatment across race and sex exist, yet there is little evidence regarding these relationships and CVD risk in Black populations. Aims: To investigate the association between various forms of childhood maltreatment (emotional, general, physical, and sexual abuse) and surrogate markers of vascular dysfunction and test whether sex modify these associations. Methods: Forms of childhood maltreatment and indices of vascular function were assessed in a cohort of healthy Black adults without known CVD (n=404) from a large metropolitan southeastern city. Childhood maltreatment was assessed using the Early Trauma Inventory short form (ETISR-SF) consisting of physical, sexual, emotional, and general domains. A trauma severity index score was calculated by summing the indexes for each of the four domains. Higher scores are indicative of higher traumatic life events before age 18 years. Outcomes of central augmentation index (cAIx) corrected for a heart rate of 75 bpm and carotid femoral pulse wave velocity (CfPWV) were measured as indices of wave reflections and arterial stiffness using applanation tonometry (Sphygmocor Inc.), and central pulse pressure (CPP) was calculated as the difference between the aortic systolic and diastolic blood pressures. Associations between each domain and outcomes were assessed using multivariate-adjusted and sex-stratified linear regression models. Results: Mean age of the cohort was 53 + 10.3 years, 61% women. Exposure to emotional [ b = -1.5%, 95%CI: -2.7,-0.3] and physical abuse [ b = -1.3%, 95%CI: -2.5,-0.2] was associated with cAIx overall but not CPP or CfPWV after adjusting for sociodemographic, clinical factors, health behaviors, and depressive symptoms. Significant emotional abuseXsex ( P= 0.014) and physical abuseXsex ( P=< 0.001) interactions were identified for cAIx. Among women, physical abuse was associated with higher cAIx [ b =1.5%, 95%CI: 0.6, 2.4] and among men emotional [ b =-1.6%, 95%CI: -3.2, -0.0] and physical [ b =-1.7%, 95%CI: -3.1, -0.3] abuse was associated with lower cAIx. Conclusions: Exposure to emotional and physical abuse as children was associated with vascular dysfunction; moreover, exposure to physical abuse among women and not men have adverse clinical implications for CVD risk and prospects for enhanced preventive care.

Is YouTube an informational source for Indian educational resources? Analysis of YouTube videos with particular reference to e-GyanKosh and e-Pgpathshala

ABSTRACT The COVID-19 pandemic created the opportunity for various educational resources to be more widely used by learners studying remotely, including in India. This study examines the availability and type of content offered by two of the most prominent Indian educational sites on YouTube. This study included content analysis, sentiment analysis, and text mining to determine the sentiment of viewers, the trend of videos, the growth of videos, and the regions where the majority of videos were generated and uploaded, among other things. This study made use of two well-known general domain educational resources, namely eGyanKosh and E-PGpathshala. Using the channel ID of each channel, Webometric Analyst software was used to collect raw data, and R-Studio was used to analyse audience sentiments on the accessible videos. Compared to subject-specific videos, the channel e-GyanKosh uploads more generic videos, such as memorials, lectures, and professors’ talks. On the other hand, more subject-specific videos are being added to e-PGpathshala. The majority of user responses were positive, indicating that viewers are satisfied with the YouTube videos made available by these two educational resources.

QOL-05. COMPARING DISEASE-SPECIFIC QUALITY-OF-LIFE OUTCOMES OF MICROSURGERY, RADIOTHERAPY, AND OBSERVATION IN VESTIBULAR SCHWANNOMA PATIENTS: A SYSTEMATIC REVIEW AND META-ANALYSIS

Abstract BACKGROUND Vestibular schwannoma (VS), also known as acoustic neuromas, are common tumors arising from the vestibulocochlear nerve and are located in the cerebellopontine angle. The benign tumor has a substantial impact on the patient-perceived quality of life (QoL) outcomes, which contribute to the decision regarding the ideal management plan. In this systematic review and meta-analysis, we aimed to compare the impact of microsurgery, radiotherapy, and observation on the QoL outcomes of VS patients. METHODS This study was conducted in accordance with the PRISMA guidelines and a total of four research databases were searched for eligible studies from inception to May 2024. The primary outcome of the study was the Penn Acoustic Neuroma Scale (PANQOL) total score and the secondary outcomes included the PANQOL subdomain scores. Data was pooled using the random-effects model and analyzed as OR (odds ratio) with 95% CI (confidence interval). RESULTS Compared to observation, radiotherapy was associated with superior QoL outcomes only in the face domain [MD (mean difference) = -4.59 [-7.29,-1.88]; I2= 85%; p = 0.0001], with observation being associated with superior outcomes across all others [PANQOL - total: MD = 4.94 [2.51,7.38]; I2= 85%; p = 0.0001]. Microsurgery, compared to radiotherapy, was associated with better outcomes only in the general domain [MD = -4.38 [-6.60,-2.16]; I2= 0%; p = 0.0001], with RT being associated with superior outcomes across all other domains [PANQOL - total: MD = 3.61 [1.29,5.93]; I2= 63%; p = 0.002]. Compared to microsurgery, observation was associated with superior QoL outcomes across all domains [PANQOL - total: MD = 9.22 [7.30,11.13]; I2= 64%; p = 0.0001]. CONCLUSION The findings indicate that, broadly, observation is associated with better QoL outcomes relative to both radiotherapy and microsurgery; with radiotherapy superseding microsurgery. However, the association with QoL improvement was not uniform across all domains.

Application of large language model combined with retrieval enhanced generation technology in digestive endoscopic nursing

BackgroundAlthough large language models (LLMs) have demonstrated powerful capabilities in general domains, they may output information in the medical field that could be incorrect, incomplete, or fabricated. They are also unable to answer personalized questions related to departments or individual patient health. Retrieval-augmented generation technology (RAG) can introduce external knowledge bases and utilize the retrieved information to generate answers or text, thereby enhancing prediction accuracy.MethodWe introduced internal departmental data and 17 commonly used gastroenterology guidelines as a knowledge base. Based on RAG, we developed the Endo-chat medical chat application, which can answer patient questions related to gastrointestinal endoscopy. We then included 200 patients undergoing gastrointestinal endoscopy, randomly divided into two groups of 100 each, for a questionnaire survey. A comparative evaluation was conducted between the traditional manual methods and Endo-chat.ResultsCompared to ChatGPT, Endo-chat can accurately and professionally answer relevant questions after matching the knowledge base. In terms of response efficiency, completeness, and patient satisfaction, Endo-chat outperformed manual methods significantly. There was no statistical difference in response accuracy between the two. Patients showed a preference for AI services and expressed support for the introduction of AI. All participating nurses in the survey believed that introducing AI could reduce nursing workload.ConclusionIn clinical practice, Endo-chat can be used as a highly effective auxiliary tool for digestive endoscopic care.

Long time behavior for the two-dimensional magnetohydrodynamic flows in a general domain

By utilizing spectral analysis methods and properties of fractional order operators, some new estimates have been established for the nonlinear terms appearing from the two-dimensional non-stationary magnetohydrodynamic flows in a general domain. Based on these, a series of energy decay properties are given. Furthermore, the large time decay behavior of the solution and that of its gradient have also been addressed in the L∞ and L2 spaces, respectively.

Influencer Marketing: A Citation and Co-citation Analysis within Business and Management Domains

Influencer marketing has emerged as a popular approach businesses use to incorporate social influence into their operations and marketing strategies. It seems to be a useful and cost-effective strategy. It ensures a broad reach to highly engaged audiences and is frequently not interpreted by consumers as advertising. Many companies use influencers on social media to market their products to certain audiences. Carryminati, Khusha Kapila, Bhuvan Bam, and Riaz are famous social media influencers. The purpose of this study is to use bibliometric analysis to compile the body of knowledge on influencer marketing in the business and management area from 2018 to 2024. BiblioShiny software was used to analyze 462 papers that were taken out of the Scopus database. This paper conducts citations and co-citations analysis, identifies recent trends, and offers useful background information on influencer marketing. The results show a notable and quick rise (54.5%) in this sector, indicating a noticeable increase in interest. The USA and Hong Kong emerged as leading nations in terms of total citations (3050) and average article citations (107.7), respectively. Social media, Commerce, Marketing, Consumer behavior, perception, and artificial intelligence are the trending topics of influencer marketing research, where artificial intelligence is the latest trend. Lou C (1650) and Yuan S (1335) are the most influential authors in terms of citation. “Journal of Retailing and Consumer Services” is the top source with maximum citations (1943) and articles (37). Various bibliometric studies have been carried out to explore the concept of influencer marketing in general or other specific domains. There is a scarcity of bibliometric research that assesses the influencer marketing literature within the business and management domain or through citation and co-citation analysis. This study addresses this research gap. These findings will have significant implications that can greatly benefit researchers, decision-makers, and professionals in crafting policies. This bibliometric study concludes with limitations, and recommendations for further study.

Arabic validation and cross-cultural adaptation of climate anxiety scale

BackgroundClimate change is an enduring global phenomenon that describes a long-lasting effect of change in weather and temperature of the earth. This study aimed to validate an Arabic version of the Climate Anxiety Scale (ACAS) to assess the anxiety associated with climate change. MethodsA cross-sectional survey was conducted using both online via Google Forms and face-to-face via hard copies, in five Arab countries, Lebanon, Palestine, Egypt, Saudia Arabia, and the United Arab Emirates). The internal consistency of the scale was assessed using the Cronbach's alpha. Exploratory factor analysis (EFA) conducted over principal component analysis assessed the scale dimensionality. Then, confirmatory factor analysis was conducted to investigate the EFA hypothesis of ACAS on anxiety about climate change. ResultsOf the 350 participants, 54.9 % were female, 77.7% lived in urban areas, 15.4% were from North Africa, 46.6 % were from Arab Gulf countries, and 38.0 % were from Bilad Al-Sham. Nearly two-thirds (62.3%) were single, 72.3% had a university degree, 94.9% were aware of climate change, 38.3 % participated in environmental protection programs, and 62.3% reported climate-related anxiety. The item content validity index (CVI) was 0.82–1.00, and the scale CVI (S-CVI) was 0.95. Overall Cronbach's alpha was 0.925 with a 95% confidence interval (CI) [0.902–0.940]. The Kaiser-Mayer-Olkin (KMO) test was 0.93, and Bartlett's test was significant (χ2 = 2762.6 p < 0.001). Bifactor model indices showed high explained common variance (ECV) (0.78), ωH (0.85), relative omega (0.91), H index (0.93), and factor determinacy (FD) (0.96) for the general factor. The general factor explained 78% of the common variance, whereas the group factors shared 22.0%. Model reliability coefficient omega (omega/omega S) for general factor, functional domain, and cognitive domain were 0.94, 0.92, and 0.89, respectively, suggesting a satisfactory fit threshold. ConclusionsThe ACAS tool is valid and reliable for assessing anxiety-related climate change among the Arab Population.

A functional exercise program improves pain and health related quality of life in patients with fibromyalgia: a randomized controlled trial

Background/objectiveFibromyalgia is a non-inflammatory syndrome characterized by generalized muscle pain, with other symptoms. Numerous forms of physical training for this population have been studied through high-quality randomized clinical trials involving strength, flexibility, aerobic conditioning and multicomponent exercise interventions. This research evaluated the effectiveness of a functional exercise program at reducing pain, improving functional capacity, increasing muscle strength as well as improving flexibility, balance and quality of life in individuals with fibromyalgia.MethodsEighty-two women with fibromyalgia were randomized into two groups. The functional exercise group performed functional exercises in 45-minute sessions twice per week for 14 weeks. The stretching exercise group performed flexibility exercises with the same duration and frequency. Outcome measures were: visual analog scale for widespread pain; Fibromyalgia Impact Questionnaire for health-related quality of life; Timed Up and Go test for functional performance; one-repetition maximum for muscle strength, Sit and Reach test on Wells bench for flexibility; Berg Balance Scale for balance; SF-36 for general quality of life.ResultsAfter the intervention, the functional exercise group had a statistically significant reduction in pain (interaction p = 0.002), and improvement in health-related quality of life measured by the Fibromyalgia Impact Questionnaire (interaction p < 0.001) and in general health state domain of SF-36 (interaction p = 0.043) compared to the stretching exercise group. No significant differences between groups were found regarding improvements in functional capacity, muscle strength, flexibility or balance.ConclusionFunctional exercise training was effective at reducing pain and improving quality of life in patients with fibromyalgia compared to stretching exercises.Trial registrationClinicalTrials.gov Identifier: NCT03682588 First prospectively registered in March 2018.

On the Hardy number of Koenigs domains

This work studies the Hardy number of hyperbolic planar domains satisfying Abel’s inclusion property, which are usually known as Koenigs domains. More explicitly, we prove that the Hardy number of a Koenings domains whose complement is non-polar is greater than or equal to 1/2, and this lower bound is sharp. In contrast to this result, we provide examples of general domains whose Hardy numbers are arbitrarily small. Additionally, we outline the connection of the aforementioned class of domains with the discrete dynamics of the unit disc and obtain results on the range of Hardy number of Koenigs maps, in the hyperbolic and parabolic case.

A comprehensive review of approaches for spatial domain recognition of spatial transcriptomes.

In current bioinformatics research, spatial transcriptomics (ST) as a rapidly evolving technology is gradually receiving widespread attention from researchers. Spatial domains are regions where gene expression and histology are consistent in space, and detecting spatial domains can better understand the organization and functional distribution of tissues. Spatial domain recognition is a fundamental step in the process of ST data interpretation, which is also a major challenge in ST analysis. Therefore, developing more accurate, efficient, and general spatial domain recognition methods has become an important and urgent research direction. This article aims to review the current status and progress of spatial domain recognition research, explore the advantages and limitations of existing methods, and provide suggestions and directions for future tool development.

Finite element analysis of a generalized Robin boundary value problem in curved domains based on the extension approach

Abstract A theoretical analysis of the finite element method for a generalized Robin boundary value problem, which involves a second-order differential operator on the boundary, is presented. If $\varOmega $ is a general smooth domain with a curved boundary, we need to introduce an approximate domain $\varOmega _{h}$ and to address issues owing to the domain perturbation $\varOmega \neq \varOmega _{h}$. In contrast to the lift approach used in existing studies, we employ the extension approach, which need not assume that boundary nodes of $\partial \varOmega _{h}$ lie exactly on $\partial \varOmega $. Assuming that approximate domains and function spaces are given by isoparametric finite elements of order $k$, we prove the optimal rate of convergence in the $H^{1}$- and $L^{2}$-norms. A numerical example is given for the piecewise linear case $k = 1$.

Association between smoking and prostate cancer survivors' long-term quality of life and function: an analysis of the CEASAR (Comparative Effectiveness Analysis of Surgery and Radiation) study.

There is limited evidence of tobacco smoking's effect on cancer survivors' quality of life (QOL) and function. As the natural history of localized prostate cancer (PCa) is protracted, there is a need to identify modifiable risk factors that can influence PCa survivorship, such as tobacco smoking. We used up to 10-year survey data from the CEASAR (Comparative Effectiveness Analysis of Surgery and Radiation) study, a prospective, population-based, observational study of patients diagnosed with localized PCa in 2011-2012. Survivors were categorized as never, former, and current smokers during survivorship. Adjusted multivariable linear regression models were used to assess the association between smoking and 5-year and 10-year scores on the 26-Item Expanded Prostate Index Composite (EPIC-26; PCa-specific domains) and 5-year scores on the Medical Outcomes Study 36-Item Short Form Survey (SF-36; general health domains). We included 2426 patients of whom 142 (6%) were current smokers, 1039 (43%) were former smokers, and 1245 (51%) were never smokers. Current smokers were more likely to be Black, low-income, and less formally educated (all p < 0.01). After adjustments, there was no association between smoking history with disease-specific functional outcomes (EPIC-26) at 5years or 10years (all p > 0.05). However, in adjusted analyses assessing general health domains (SF-36), compared to participants who never smoked, current smokers during survivorship had worse physical function (- 10.96, 95% CI - 16.37 to - 5.55, p < 0.01) at 5years. PCa survivors who continue to smoke experience worse physical functioning though there is no significant independent effect on PCa-specific functional domains. Prostate cancer survivors who continue to smoke experience worse physical functioning though there is no significant independent effect on PCa-specific functional domains. Smoking cessation may improve prostate cancer survivorship.

Introducing high correlation and high quality instances for few-shot entity linking

Entity linking, the process of connecting textual mentions in documents to canonical entities within a knowledge base, plays an integral role in a myriad of natural language processing tasks. A significant challenge prevalent within the field is the scarcity of resources, particularly for multiple specialized domains, which accentuates the importance of few-shot entity linking in real-world scenarios. Previous works address the problem of lacking in-domain labeled data by generating synthetic data. However, we argue that the synthetic data is frequently far from high-quality, such low-quality instances will introduce noise and diminish the ability of entity linking models to comprehend the semantic consistency between mentions and entities. In this paper, we propose a H2FEL framework to introduce high correlation and high quality instances for few-shot entity linking. We argue that there are rich high-quality labeled data in general domains and some of them are highly correlated to the target domain. Thus, we first design an adversarial instance extraction module to extract such high-correlation instances without depending on additional manually annotated data. To further mitigate the negative effects brought by low-correlation instances, we train our entity linking model via a variant of curriculum learning. Experimental results on the few-shot entity linking dataset demonstrate the effectiveness of our proposed H2FEL framework and it achieves state-of-the-art performance.

Relationships between self-efficacy beliefs and personal factors in final-year medical students

BackgroundMedical students entering postgraduate training often feel ill-prepared for clinical practice. This has implications for physician well-being and the quality of patient care. Self-efficacy represents an important contributing factor in the transition process. Identifying student characteristics associated with self-efficacy is important in order to design possible interventions to enhance self-efficacy. Therefore, we assessed the relationship between final-year medical students’ general self-efficacy with their personality traits, need for cognitive closure, and medical knowledge in order to identify factors contributing to the assessment and improvement of self-efficacy.MethodsSelf-assessed data regarding general self-efficacy was collected from 188 final-year medical students who participated in a first day of residency simulation between March and September 2023. We also assessed several personal factors including the HEXACO Personality Inventory Revised (HEXACO-PI-R), a short scale to measure Need for Cognitive Closure (16-NCCS), and students’ medical exam grades (M2). We ran multiple correlation and multiple linear regression analyses across all variables. We then added a second analysis each with general self-efficacy and HEXACO domains Emotionality and Extraversion on facet level, which both showed significant associations with self-efficacy in the respective first analysis.ResultsAcross all variables, self-efficacy correlated significantly negatively with Emotionality and positively with Extraversion. Both domains also significantly predicted self-efficacy accordingly in the regression model. On a facet analysis, Sentimentality (Emotionality) and Sociability (Extraversion) did not correlate significantly with self-efficacy. In the regression model only the facets Sociability (Extraversion) and Fearfulness (Emotionality) significantly predicted lower self-efficacy. Furthermore, self-efficacy correlated significantly and negatively with the Need for Cognitive Closure (NCC). Additionally, significant positive and negative correlations of the NCC with both Emotionality and Extraversion, respectively, as well as a significant positive correlation for exam grades (M2) and Extraversion were found.ConclusionsSelf-efficacy contributes to medial students’ preparedness for clinical practice and can be predicted by several personal factors. Students should learn and reflect about these factors and need to be supported in increasing their self-efficacy and its self-assessment towards their transition to postgraduate training. Construction of a specific instrument for self-efficacy assessment utilizing the insight provided by the findings is proposed as a next step.

Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders

Deep neural networks are increasingly used in medical imaging for tasks such as pathological classification, but they face challenges due to the scarcity of high-quality, expert-labeled training data. Recent efforts have utilized pre-trained contrastive image-text models like CLIP, adapting them for medical use by fine-tuning the model with chest X-ray images and corresponding reports for zero-shot pathology classification, thus eliminating the need for pathology-specific annotations. However, most studies continue to use the same contrastive learning objectives as in the general domain, overlooking the multi-labeled nature of medical image-report pairs. In this paper, we propose a new fine-tuning strategy that includes positive-pair loss relaxation and random sentence sampling. We aim to improve the performance of zero-shot pathology classification without relying on external knowledge. Our method can be applied to any pre-trained contrastive image-text encoder and easily transferred to out-of-domain datasets without further training, as it does not use external data. Our approach consistently improves overall zero-shot pathology classification across four chest X-ray datasets and three pre-trained models, with an average macro AUROC increase of 4.3%. Additionally, our method outperforms the state-of-the-art and marginally surpasses board-certified radiologists in zero-shot classification for the five competition pathologies in the CheXpert dataset.

A comprehensive review of existing corpora and methods for creating annotated corpora for event extraction tasks

Abstract Purpose The purpose of this study is to serve as a comprehensive review of the existing annotated corpora. This review study aims to provide information on the existing annotated corpora for event extraction, which are limited but essential for training and improving the existing event extraction algorithms. In addition to the primary goal of this study, it provides guidelines for preparing an annotated corpus and suggests suitable tools for the annotation task. Design/methodology/approach This study employs an analytical approach to examine available corpus that is suitable for event extraction tasks. It offers an in-depth analysis of existing event extraction corpora and provides systematic guidelines for researchers to develop accurate, high-quality corpora. This ensures the reliability of the created corpus and its suitability for training machine learning algorithms. Findings Our exploration reveals a scarcity of annotated corpora for event extraction tasks. In particular, the English corpora are mainly focused on the biomedical and general domains. Despite the issue of annotated corpora scarcity, there are several high-quality corpora available and widely used as benchmark datasets. However, access to some of these corpora might be limited owing to closed-access policies or discontinued maintenance after being initially released, rendering them inaccessible owing to broken links. Therefore, this study documents the available corpora for event extraction tasks. Research limitations Our study focuses only on well-known corpora available in English and Chinese. Nevertheless, this study places a strong emphasis on the English corpora due to its status as a global lingua franca, making it widely understood compared to other languages. Practical implications We genuinely believe that this study provides valuable knowledge that can serve as a guiding framework for preparing and accurately annotating events from text corpora. It provides comprehensive guidelines for researchers to improve the quality of corpus annotations, especially for event extraction tasks across various domains. Originality/value This study comprehensively compiled information on the existing annotated corpora for event extraction tasks and provided preparation guidelines.

The impact of closed suction wound drainage on chronic pain and health-related quality of life after posterior spinal fusion in patients with adolescent idiopathic scoliosis.

Closed suction subfascial drainage is widely used after instrumented posterior spinal fusion in patients with a spinal deformity. The aim of this study was to determine the effect of this wound drainage on the outcomes in patients with adolescent idiopathic scoliosis (AIS). This was a further analysis of a randomized, multicentre clinical trial reporting on patients after posterior spinal fusion using segmental pedicle screw instrumentation. In this study the incidence of deep surgical site infection (SSI) and chronic postoperative pain at two years' follow-up are reported. We conducted a randomized, multicentre clinical trial on adolescents undergoing posterior spinal fusion for AIS using segmental pedicle screw instrumentation. A total of 90 consecutive patients were randomized into a 'drain' or 'no drain' group at the time of wound closure, using the sealed envelope technique (1:1). The primary outcomes in the initial study were the change in the level of haemoglobin in the blood postoperatively and total blood loss. A secondary outcome was the opioid consumption immediately after surgery. The aim of this further study was to report the rate of deep SSI and persistent postoperative pain, at two years' follow-up. As previously reported, the mean 48-hour opioid consumption was significantly higher in the no drain group (2.0 mg/kg (SD 0.8) vs 1.4 mg/kg (SD 0.7); p = 0.005). There were no delayed deep SSIs. At two years' follow-up, the mean Scoliosis Research Society 24-item questionnaire (SRS-24) total score did not differ between the groups (4.11 (SD 0.47) vs 4.16 (SD 0.33); p = 0.910). The mean SRS-24 pain score was 4.28 (SD 0.48) in the no drain group compared with 4.33 (SD 0.66) in the drain group (p = 0.245). Seven patients (19%) in the no drain group and six in the drain group (14%) reported moderate to severe pain (numerical rating scale ≥ 4) at two years' follow-up (p = 0.484). The general self-image domain score was significantly higher in the no drain group compared with the drain group (4.02 (SD 0.74) vs 4.59 (SD 0.54); p < 0.001). The main finding in this study was that more severe pain immediately after surgery did not result in a higher incidence of chronic pain in the no drain group compared with the drain group. Back pain and health-related quality of life at two years' follow-up did not differ between the groups. The patient-reported self-image domain was significantly better in the no drain group compared with the drain group.

Asymptotic analysis of the 2D and 3D convective Brinkman–Forchheimer equations in unbounded domains

In this work, we carry out the asymptotic analysis of two- and three-dimensional convective Brinkman–Forchheimer (CBF) equations, which characterize the motion of incompressible fluid flows in a saturated porous medium. We establish the existence of a global attractor in both bounded (using compact embedding) and unbounded Poincaré domains (using asymptotic compactness property). In Poincaré domains, the estimates for Hausdorff as well as fractal dimensions of the global attractors are also obtained. We then show an upper semicontinuity of global attractors with respect to domains for CBF equations. We consider an expanding sequence of simply connected, bounded and smooth subdomains [Formula: see text] of the Poincaré domain [Formula: see text] such that [Formula: see text] as [Formula: see text]. If [Formula: see text] and [Formula: see text] are the global attractors of CBF equations corresponding to [Formula: see text] and [Formula: see text], respectively, then we show that for large enough [Formula: see text], the global attractor [Formula: see text] enters into any neighborhood [Formula: see text] of [Formula: see text]. The presence of the Darcy term in CBF equations helps us to obtain the above mentioned results in general unbounded domains also. Finally, we discuss the quasi-stability property of the semigroup associated with CBF equations in bounded domains and establish the existence of finite fractal dimensional global as well as exponential attractors.

Comparative Analysis of Large Language Models in Chinese Medical Named Entity Recognition.

The emergence of large language models (LLMs) has provided robust support for application tasks across various domains, such as name entity recognition (NER) in the general domain. However, due to the particularity of the medical domain, the research on understanding and improving the effectiveness of LLMs on biomedical named entity recognition (BNER) tasks remains relatively limited, especially in the context of Chinese text. In this study, we extensively evaluate several typical LLMs, including ChatGLM2-6B, GLM-130B, GPT-3.5, and GPT-4, on the Chinese BNER task by leveraging a real-world Chinese electronic medical record (EMR) dataset and a public dataset. The experimental results demonstrate the promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for Chinese BNER tasks. More importantly, instruction fine-tuning significantly enhances the performance of LLMs. The fine-tuned offline ChatGLM2-6B surpassed the performance of the task-specific model BiLSTM+CRF (BC) on the real-world dataset. The best fine-tuned model, GPT-3.5, outperforms all other LLMs on the publicly available CCKS2017 dataset, even surpassing half of the baselines; however, it still remains challenging for it to surpass the state-of-the-art task-specific models, i.e., Dictionary-guided Attention Network (DGAN). To our knowledge, this study is the first attempt to evaluate the performance of LLMs on Chinese BNER tasks, which emphasizes the prospective and transformative implications of utilizing LLMs on Chinese BNER tasks. Furthermore, we summarize our findings into a set of actionable guidelines for future researchers on how to effectively leverage LLMs to become experts in specific tasks.