Global Antigenic Landscape and Vaccine Recommendation Strategy for Low Pathogenic Avian Influenza A(H9N2) Viruses.

The sustained circulating of H9N2 avian influenza viruses (AIVs) poses a significant threat for contributing to a new pandemic. Given the temporal and spatial uncertainty in antigenicity of H9N2 AIVs, the immune protection efficiency of vaccines remains challenging. By developing an antigenicity prediction method for H9N2 AIVs, named PREDAC-H9, the global antigenic landscape of H9N2 AIVs was mapped. PREDAC-H9 utilizes the XGBoost model with 14 well-designed features. The XGBoost model was built and evaluated to predict the antigenic relationship between any two viruses with high values of 81.1%, 81.4%, 81.3%, 81.1%, and 89.4% in accuracy, precision, recall, F1 value, and area under curve (AUC), respectively. Then the antigenic correlation network (ACnet) was constructed based on the predicted antigenic relationship for H9N2 AIVs from 1966 to 2022, and ten major antigenic clusters were identified. Of these, four novel clusters were generated in China in the past decade, demonstrating the unique complex situation there. To help tackle this situation, we applied PREDAC-H9 to calculate the cluster-transition determining sites and screen out virus strains with high cross-protective spectrum, thus providing in-silico reference for vaccine recommendation. The proposed model will reduce the clinical monitoring workload and provide useful tool for surveillance and control of H9N2 AIVs. AVAILABILITY OF DATA AND MATERIALS: The data that support the findings of this study are available in the Supplementary Data.

A Recommendation System for Prosumers Based on Large Language Models.

As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home's comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by large language models (LLMs), Scikit-llm and zero-shot classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy. A comparison with a content-based filtering system is provided considering the performance metrics that are relevant for prosumers.

Rating of Perceived Exertion: A Large Cross-Sectional Study Defining Intensity Levels for Individual Physical Activity Recommendations.

BACKGROUND: Physical inactivity is a growing risk factor worldwide, therefore getting people into sports is necessary. When prescribing physical activity, it is essential to recommend the correct training intensities. Cardiopulmonary exercise testing (CPX) enables precise determination of individuals' training intensities but is unavailable for a broad population. Therefore, the Borg scale allows individuals to assess perceived exertion and set their intensity easily and cost-efficiently. In order to transfer CPX to rating of perceived exertion (RPE), previous studies investigated RPE on specific physiological anchors, e.g. blood lactate (bLa) concentrations, but representativeness for a broad population is questionable. Some contradictory findings regarding individual factors influencing RPE occur, whereas univariable analysis has been performed so far. Moreover, a multivariable understanding of individual factors influencing RPE is missing. This study aims to determine RPE values at the individual anaerobic threshold (LT2) and defined bLa concentrations in a large cohort and to evaluate individual factors influencing RPE with multivariable analysis. METHODS: CPX with bicycle or treadmill ergometer of 6311 participants were analyzed in this cross-sectional study. RPE values at bLa concentrations 2 mmol/l, 3 mmol/l, 4 mmol/l, and LT2 (first rise in bLa over baseline + 1.5 mmol/l) were estimated by spline interpolation. Multivariable cumulative ordinal regression models were performed to assess the influence of sex, age, type of ergometry, VO2max, and duration of exercise testing on RPE. RESULTS: Median values [interquartile range (IQR)] of the total population were RPE 13 [11; 14] at 2 mmol/l, RPE 15 [13; 16] at 3 mmol/l, RPE 16 [15; 17] at 4 mmol/l, and RPE 15 [14; 16] at LT2. Main influence of individual factors on RPE were seen especially at 2 mmol/l: male sex (odds ratio (OR) [95%-CI]: 0.65 [0.587; 0.719]), treadmill ergometry (OR 0.754 [0.641; 0.886]), number of stages (OR 1.345 [1.300; 1.394]), age (OR 1.015 [1.012; 1.018]), and VO2max (OR 1.023 [1.015; 1.030]). Number of stages was the only identified influencing factor on RPE at all lactate concentrations/LT2 (3 mmol/l: OR 1.290 [1.244; 1.336]; 4 mmol/l: OR 1.229 [1.187; 1.274]; LT2: OR 1.155 [1.115; 1.197]). CONCLUSION: Our results suggest RPE ≤ 11 for light intensity, RPE 12-14 for moderate intensity, and RPE 15-17 for vigorous intensity, which slightly differs from the current American College of Sports Medicine (ACSM) recommendations. Additionally, we propose an RPE of 15 delineating heavy and severe intensity domain. Age, sex, type of ergometry, duration of exercise, and cardiopulmonary fitness should be considered when recommending individualized intensities with RPE, primarily at lower intensities. Therefore, this study can be used as a new guideline for prescribing individual RPE values in the clinical practice, predominantly for endurance type exercise.

Prognostic factors of patients with thyroid cancer and bone metastasis at presentation.

Objective: While bone metastases (BMs) are present in a minority of thyroid cancer (TC) patients at the time of initial diagnosis, there has been growing concern regarding their impact on life expectancy and quality of life. The aim of this study was to identify prognostic factors associated with overall survival (OS) and cancer-specific survival (CSS) in these patients and provide therapeutic recommendations based on the findings. Methods: In this retrospective cohort study, we included 82 patients diagnosed as TC with BM received treatment in our department from 2011.03 to 2023.03 (average follow-up duration was 3.02 years). The retrospective study was performed according to the inclusion and exclusion criteria. Kaplan-Meier analysis was used to estimate the OS and CSS, while the univariate and multivariate Cox proportional hazard models were employed to determine prognostic factors associated with OS and CSS. Also, 287 patients' data were collected from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 to confirm the prognostic factors identified in the retrospective study. Results: The average survival time of the 82 patients was estimated to be 5.818 years (with a 95% confidence interval (CI) of 4.767 to 6.868 years). The cox regression analysis showed that older age (hazard ratio (HR) = 1.045, 95% CI: 1.001-1.092, P = 0.047), larger tumor size (>5cm, HR = 11.087, 95% CI: 3.728 - 32.976, P = 0.000), and the presence of extraosseous metastasis (HR = 3.247, 95% CI: 1.376 - 7.665, P = 0.007) were statistically significant factors associated with worse CSS. The results were furtherly confirmed in 287 SEER-sourced patients (age (HR = 1.020, 95% CI: 1.006 - 1.034, P = 0.006), tumor size (HR = 2.917, 95% CI: 2.044 - 4.161, P = 0.000), and extraosseous metastasis (HR = 3.726, 95% CI: 2.571 - 5.398, P = 0.000)). Conclusions: These results offer a population-based assessment of prognostic factors for patients with TC and BMs, revealing that age, primary tumor size (>5cm), and presence of extraosseous metastases are independent prognostic factors that correlate with worse survival. Accordingly, treatment for such patients ought to concentrate on systemic integrative therapy instead of surgical intervention.

Systematic analysis of levels of evidence supporting Chinese clinical practice guidelines for gastrointestinal disease.

BACKGROUND: Clinical practice guidelines (CPGs) inform healthcare decisions and improve patient care. However, an evaluation of guidelines on gastrointestinal diseases (GIDs) is lacking. This study aimed to systematically analyze the level of evidence (LOE) supporting Chinese CPGs for GIDs. METHODS: CPGs for GIDs were identified by systematically searching major databases. Data on LOEs and classes of recommendations (CORs) were extracted. According to the Grades of Recommendation, Assessment, Development, and Evaluation system, LOEs were categorized as high, moderate, low, or very low, whereas CORs were classified as strong or weak. Statistical analyses were conducted to determine the distribution of LOEs and CORs across different subtopics and assess changes in evidence quality over time. FINDINGS: Only 27.9% of these recommendations were supported by a high LOE, whereas approximately 70% were strong recommendations. There was a significant disparity among different subtopics in the proportion of strong recommendations supported by a high LOE. The number of guidelines has increased in the past 5 years, but there has been a concomitant decline in the proportion of recommendations supported by a high LOE. CONCLUSIONS: There is a general lack of high-quality evidence supporting Chinese CPGs for GIDs, and there are inconsistencies in strong recommendations that have not improved. This study identified areas requiring further research, emphasizing the need to bridge these gaps and promote the conduct of high-quality clinical trials. FUNDING: This study was supported by the National Key R&D Program of China (2022YFC2503604 and 2022YFC2503605) and Special Topics in Military Health Care (22BJZ25).

Evolution in the management of thyroid surgery over a period of 15 years in a Belgian center.

BACKGROUND: Guidelines for thyroid surgery have evolved to reflect advances in medical knowledge and decrease the overdiagnosis of low-risk thyroid cancer. Our goal was to analyze the change made in operative thyroid management and the impact on thyroid cancer diagnosis. BACKGROUND: Guidelines for thyroid surgery have evolved to reflect advances in medical knowledge and decrease overdiagnosis of low risk thyroid cancer. Our goal was to study the evolution, over a long period, of pre- and postoperative management and the influence on histological cancer diagnosis and, more particularly, microcancer. METHODS: In this retrospective cohort study, we included 891 consecutive patients who underwent thyroid surgery between 2007 and 2020. RESULTS: Respectively 305, 290 and 266 patients underwent surgery over the 3 periods of 2007-2010, 2011-2015 and 2016-2020. In all three periods, women represented approximately 70% of the population, and the mean age was 54 years old (range: 67). Most surgeries (90%) involved total thyroidectomies. Over the study period, the proportion of preoperative fine needle aspiration (FNA) increased from 13 to 55%, p < 0,01. Cancer was found in a total of 116 patients: 35 (11%) patients between 2007 and 2010, 50 (17%) between 2011 and 2015 and 32 (12%) between 2016 and 2020 (p = 0.08). For all 3 periods, papillary thyroid cancer (PTC) was the most prevalent, at approximately 80%. The proportion of thyroid cancer > T1a increased significantly from 37% (2011-2015 period) to 81% (2016-2020 period), p = 0.001. Patients treated with radioiodine remained relatively stable (approximately 60%) but were more frequently treated with a low dose of radioiodine (p < 0.01) and recombinant human TSH (p < 0.01). Operative thyroid weight decreased over time in all but the low-risk T1a PTC cases. CONCLUSIONS: Over a period of 15 years and according to the evolution of recommendations, the care of patients who underwent thyroid surgery changed with the increased use of preoperative FNA. This change came with a decrease in low-risk T1a PTC.

A time-robust group recommender for featured comments on news platforms.

Introduction: Recently, content moderators on news platforms face the challenging task to select high-quality comments to feature on the webpage, a manual and time-consuming task exacerbated by platform growth. This paper introduces a group recommender system based on classifiers to aid moderators in this selection process. Methods: Utilizing data from a Dutch news platform, we demonstrate that integrating comment data with user history and contextual relevance yields high ranking scores. To evaluate our models, we created realistic evaluation scenarios based on unseen online discussions from both 2020 and 2023, replicating changing news cycles and platform growth. Results: We demonstrate that our best-performing models maintain their ranking performance even when article topics change, achieving an optimum mean NDCG@5 of 0.89. Discussion: The expert evaluation by platform-employed moderators underscores the subjectivity inherent in moderation practices, emphasizing the value of recommending comments over classification. Our research contributes to the advancement of (semi-)automated content moderation and the understanding of deliberation quality assessment in online discourse.

Cracking the Code: Interpreting Content and Phrases Used in Maternal-Fetal Medicine Fellowship Letters of Recommendation.

BACKGROUND: Letters of recommendation for Maternal-Fetal Medicine(MFM) fellowship are a critical part of the applicant selection process. However, data regarding best practices for how to write LOR for MFM is limited. Similarly, within letters of recommendation, differences in the 'code' or meaning of summative words/phrases used at the end of letters of recommendation are seen between surgery, pediatrics and medicine. However, data regarding code MFM Letters of recommendation are quite limited. OBJECTIVE: We sought to describe what Maternal-Fetal Medicine program directors value in letters of recommendation for fellowship applicants and how PDs interpret commonly used summative words/phrases. STUDY DESIGN: After IRB exemption, subject matter experts developed an e-survey querying the importance of various letters of recommendation 'best practices' described by other specialties. Content and face validation were performed prior to dissemination. This cross-sectional survey was administered to MFM program directors in February 2023. The primary outcome was the relative importance of letters of recommendation content areas. Secondary outcomes included the strength of each summative 'code' phrase. Descriptive analysis was performed and principal component analysis (PCA) was then used to reduce the list of phrases to their underlying dimensions. Statistical analysis was performed by SPSS 29.0. RESULTS: Of 104 MFM program directors sent the survey, 70 (67%) responded. MFM program directors reviewed an average of 78 applications (SD, 30) with 60% writing ≥3 letters/year. Ninety-one percent of respondents noted that letters of recommendation are important/very important in shaping impressions of an applicant. Respondents reported the depth of interaction with an applicant, the applicant's specific behavior traits, the applicant's abilities and a summative statement including strength of the recommendation as important content for MFM fellowship letters of recommendation. Letter length, use of bold/italics, and restating the applicant's curriculum vitae were considered not important. Following PCA with varimax rotation, 14 specific phrases used in letters of recommendation were reduced to 5 themes: high qualitative assessments, average qualitative assessments, objective metrics, exceeding expectations and grit. These themes accounted for 64.6% of the variance in the model (KMO 0.7, Bartlett's Test of Sphericity p<0.01). Phrases that respondents considered positive included: 'Top 5%', 'Want to keep', and 'highest recommendation', (all mean score≥4.5/5), while 'expected level', 'showed improvement', and '2nd quartile' were negatively associated code words (all mean score <2.5/5). CONCLUSIONS: MFM program directors reported that descriptions of an applicant's abilities, behavior traits, and depth of the writer's interactions with the applicant were all important components of an MFM fellowship letters of recommendation. Letter length, bold/italics, and highlights from the CV were not important. A clear 'code' emerged regarding summative phrases included in letters of recommendation. Dissemination of these data might help less experienced letter writers send a clearer message and ensure all letter writers have a shared mental model.

RefAI: a GPT-powered retrieval-augmented generative tool for biomedical literature recommendation and summarization.

OBJECTIVES: Precise literature recommendation and summarization are crucial for biomedical professionals. While the latest iteration of generative pretrained transformer (GPT) incorporates 2 distinct modes-real-time search and pretrained model utilization-it encounters challenges in dealing with these tasks. Specifically, the real-time search can pinpoint some relevant articles but occasionally provides fabricated papers, whereas the pretrained model excels in generating well-structured summaries but struggles to cite specific sources. In response, this study introduces RefAI, an innovative retrieval-augmented generative tool designed to synergize the strengths of large language models (LLMs) while overcoming their limitations. MATERIALS AND METHODS: RefAI utilized PubMed for systematic literature retrieval, employed a novel multivariable algorithm for article recommendation, and leveraged GPT-4 turbo for summarization. Ten queries under 2 prevalent topics ("cancer immunotherapy and target therapy" and "LLMs in medicine") were chosen as use cases and 3 established counterparts (ChatGPT-4, ScholarAI, and Gemini) as our baselines. The evaluation was conducted by 10 domain experts through standard statistical analyses for performance comparison. RESULTS: The overall performance of RefAI surpassed that of the baselines across 5 evaluated dimensions-relevance and quality for literature recommendation, accuracy, comprehensiveness, and reference integration for summarization, with the majority exhibiting statistically significant improvements (P-values <.05). DISCUSSION: RefAI demonstrated substantial improvements in literature recommendation and summarization over existing tools, addressing issues like fabricated papers, metadata inaccuracies, restricted recommendations, and poor reference integration. CONCLUSION: By augmenting LLM with external resources and a novel ranking algorithm, RefAI is uniquely capable of recommending high-quality literature and generating well-structured summaries, holding the potential to meet the critical needs of biomedical professionals in navigating and synthesizing vast amounts of scientific literature.

Developing a Personalized Meal Recommendation System for Chinese Older Adults: Observational Cohort Study.

BACKGROUND: China's older population is facing serious health challenges, including malnutrition and multiple chronic conditions. There is a critical need for tailored food recommendation systems. Knowledge graph-based food recommendations offer considerable promise in delivering personalized nutritional support. However, the integration of disease-based nutritional principles and preference-related requirements needs to be optimized in current recommendation processes. OBJECTIVE: This study aims to develop a knowledge graph-based personalized meal recommendation system for community-dwelling older adults and to conduct preliminary effectiveness testing. METHODS: We developed ElCombo, a personalized meal recommendation system driven by user profiles and food knowledge graphs. User profiles were established from a survey of 96 community-dwelling older adults. Food knowledge graphs were supported by data from websites of Chinese cuisine recipes and eating history, consisting of 5 entity classes: dishes, ingredients, category of ingredients, nutrients, and diseases, along with their attributes and interrelations. A personalized meal recommendation algorithm was then developed to synthesize this information to generate packaged meals as outputs, considering disease-related nutritional constraints and personal dietary preferences. Furthermore, a validation study using a real-world data set collected from 96 community-dwelling older adults was conducted to assess ElCombo's effectiveness in modifying their dietary habits over a 1-month intervention, using simulated data for impact analysis. RESULTS: Our recommendation system, ElCombo, was evaluated by comparing the dietary diversity and diet quality of its recommended meals with those of the autonomous choices of 96 eligible community-dwelling older adults. Participants were grouped based on whether they had a recorded eating history, with 34 (35%) having and 62 (65%) lacking such data. Simulation experiments based on retrospective data over a 30-day evaluation revealed that ElCombo's meal recommendations consistently had significantly higher diet quality and dietary diversity compared to the older adults' own selections (P<.001). In addition, case studies of 2 older adults, 1 with and 1 without prior eating records, showcased ElCombo's ability to fulfill complex nutritional requirements associated with multiple morbidities, personalized to each individual's health profile and dietary requirements. CONCLUSIONS: ElCombo has shown enhanced potential for improving dietary quality and diversity among community-dwelling older adults in simulation tests. The evaluation metrics suggest that the food choices supported by the personalized meal recommendation system surpass autonomous selections. Future research will focus on validating and refining ElCombo's performance in real-world settings, emphasizing the robust management of complex health data. The system's scalability and adaptability pinpoint its potential for making a meaningful impact on the nutritional health of older adults.

Enabling Health Information Recommendation Using Crowdsourced Refinement in Web-Based Health Information Applications: User-Centered Design Approach and EndoZone Informatics Case Study.

BACKGROUND: In the digital age, search engines and social media platforms are primary sources for health information, yet their commercial interests-focused algorithms often prioritize irrelevant content. Web-based health applications by reputable sources offer a solution to circumvent these biased algorithms. Despite this advantage, there remains a significant gap in research on the effective integration of content-ranking algorithms within these specialized health applications to ensure the delivery of personalized and relevant health information. OBJECTIVE: This study introduces a generic methodology designed to facilitate the development and implementation of health information recommendation features within web-based health applications. METHODS: We detail our proposed methodology, covering conceptual foundation and practical considerations through the stages of design, development, operation, review, and optimization in the software development life cycle. Using a case study, we demonstrate the practical application of the proposed methodology through the implementation of recommendation functionalities in the EndoZone platform, a platform dedicated to providing targeted health information on endometriosis. RESULTS: Application of the proposed methodology in the EndoZone platform led to the creation of a tailored health information recommendation system known as EndoZone Informatics. Feedback from EndoZone stakeholders as well as insights from the implementation process validate the methodology's utility in enabling advanced recommendation features in health information applications. Preliminary assessments indicate that the system successfully delivers personalized content, adeptly incorporates user feedback, and exhibits considerable flexibility in adjusting its recommendation logic. While certain project-specific design flaws were not caught in the initial stages, these issues were subsequently identified and rectified in the review and optimization stages. CONCLUSIONS: We propose a generic methodology to guide the design and implementation of health information recommendation functionality within web-based health information applications. By harnessing user characteristics and feedback for content ranking, this methodology enables the creation of personalized recommendations that align with individual user needs within trusted health applications. The successful application of our methodology in the development of EndoZone Informatics marks a significant progress toward personalized health information delivery at scale, tailored to the specific needs of users.

Fitness-to-drive recommendations in post-stroke patients: a retrospective study.

BACKGROUND: Stroke sequelae can have an impact on daily life activities such as driving. French legislation stipulates that post-stroke patients should undergo a multi-professional fitness-to-drive assessment before being allowed to drive again. This retrospective study aims to explore the determinants of multi-professional fitness-to-drive recommendations. METHODS: Sixty-six post-stroke patients assessed for fitness to drive in the Kerpape Center, France in 2019 were included. Favorable or unfavorable driving recommendations were attributed to patients following a joint decision by a multi-professional team. Individual characteristics obtained from medical records were compared. RESULTS: Findings showed that 64% of stroke patients received a favorable fitness-to-drive recommendation. Across all demographic, clinical, and driving characteristics, the time interval between stroke and assessment was significantly longer for patients designated as unfit to drive than for those designated as fit to drive (P = .004). Furthermore, the proportion of instrumental sequelae was higher in patients designated as unfit to drive than in those designated as fit to drive (P = .022). Stepwise logistic regression showed that the presence of instrumental sequelae, mainly aphasia, was the best predictor of fitness-to-drive recommendations. CONCLUSIONS: The post-stroke time interval and the presence of instrumental sequelae explained the difference between patients recommended as fit-to-drive and unfit-to-drive. Furthermore, aphasia was found be the best predictor of a fitness-to-drive recommendation. It is possible that aphasia impacts the understanding of instructions during on-road testing. These findings emphasize the need for a standardized multi-professional fitness-to-drive assessment, since the determinants of fitness-to-drive recommendation differ between studies.

Knowledge enhanced attention aggregation network for medicine recommendation.

The combination of deep learning and the medical field has recently achieved great success, particularly in recommending medicine for patients. However, patients' clinical records often contain repeated medical information that can significantly impact their health condition. Most existing methods for modeling longitudinal patient information overlook the impact of individual diagnoses and procedures on the patient's health, resulting in insufficient patient representation and limited accuracy of medicine recommendations. Therefore, we propose a medicine recommendation model called KEAN, which is based on an attention aggregation network and enhanced graph convolution. Specifically, KEAN can aggregate individual diagnoses and procedures in patient visits to capture significant features that affect patients' diseases. We further incorporate medicine knowledge from complex medicine combinations, reduce drug-drug interactions (DDIs), and recommend medicines that are beneficial to patients' health. The experimental results on the MIMIC-III dataset demonstrate that our model outperforms existing advanced methods, which highlights the effectiveness of the proposed method.

Methodological proposals for developing trustworthy recommendations of integrative Chinese-Western medicine.

Background: To refine the methods of developing clinical practice guidelines (CPGs) for integrative Chinese-Western medicine (ICWM), promoting the formation of trustworthy, implementable recommendations that integrate the strengths of Chinese and Western medicine. Methods: Using a nominal group technique (NGT) approach, a multidisciplinary expert panel was established. The panel identified key methodological issues in ICWM-CPG development through literature review and iterative discussions, and formulated methodological proposals to address these issues. The final set of proposals was achieved through consensus among the panel members. Results: The collaborative effort resulted in the identification of five pivotal methodological issues and the subsequent establishment of 22 specific recommendations. These encompass strict adherence to renowned standards, such as those proposed by the Institute of Medicine (IOM) and Guidelines International Network (G-I-N), the employment of methodologies like the GRADE approach and RIGHT statement, the strategic constitution of a balanced development group, the adept identification of ICWM-focused clinical inquiries, the nuanced integration of diverse evidence sources, and the detailed crafting of transparent, implementable recommendations. Conclusions: This study concentrates on the most crucial and prevalent methodological issues in ICWM-CPG development, proposing a series of recommendations. These suggestions result from a multidisciplinary expert consensus, aiming to provide methodological guidance for ICWM-CPG developers, building upon the current foundational methodologies.

Regional differences in provider recommendation of HPV vaccines among 13-to-17 year old adolescents from 2019 to 2021.

PURPOSE: The prevalence of recommendation of human papillomavirus (HPV) vaccination by health care providers has improved over the last decade. However, research to determine whether the COVID-19 pandemic affected the progress in recommendation among adolescents across the U.S. regions has been limited. Therefore, the present study was conducted to determine if region was associated with provider recommendation of HPV vaccines in 2019-2021 and whether changes in recommendations varied by region. METHOD: Using a cross-sectional design to examine National Immunization Survey-Teen (2019-2021) data, logistic regression and moderation analyses were performed to model region variation in HPV vaccine recommendations (n = 50,739 respondents). RESULTS: The odds of recommendation were higher in the Midwest (aOR, 1.17 [95% CI, 1.06-1.29]), and Northeast (aOR, 1.38 [95% CI, 1.23-1.56]) regions than in the South region. Also, the odds of provider recommendation were higher in 2020 (aOR,1.16 [95% CI, 1.03-1.30]) than in 2019. Other variables-sex, age, race/ethnicity, health insurance status, and poverty status-were associated with recommendation of HPV vaccination. CONCLUSION: Although the improvement in recommendation from 2019 to 2020 is an important public health gain, recommendation in the South still lags behind that in other regions. More efforts are needed to improve HPV vaccination recommendations in this region.

Individualized survival prediction and surgery recommendation for patients with glioblastoma.

Background: There is a lack of individualized evidence on surgical choices for glioblastoma (GBM) patients. Aim: This study aimed to make individualized treatment recommendations for patients with GBM and to determine the importance of demographic and tumor characteristic variables in the selection of extent of resection. Methods: We proposed Balanced Decision Ensembles (BDE) to make survival predictions and individualized treatment recommendations. We developed several DL models to counterfactually predict the individual treatment effect (ITE) of patients with GBM. We divided the patients into the recommended (Rec.) and anti-recommended groups based on whether their actual treatment was consistent with the model recommendation. Results: The BDE achieved the best recommendation effects (difference in restricted mean survival time (dRMST): 5.90; 95% confidence interval (CI), 4.40-7.39; hazard ratio (HR): 0.71; 95% CI, 0.65-0.77), followed by BITES and DeepSurv. Inverse probability treatment weighting (IPTW)-adjusted HR, IPTW-adjusted OR, natural direct effect, and control direct effect demonstrated better survival outcomes of the Rec. group. Conclusion: The ITE calculation method is crucial, as it may result in better or worse recommendations. Furthermore, the significant protective effects of machine recommendations on survival time and mortality indicate the superiority of the model for application in patients with GBM. Overall, the model identifies patients with tumors located in the right and left frontal and middle temporal lobes, as well as those with larger tumor sizes, as optimal candidates for SpTR.

Integrating AI in Lipedema Management: Assessing the Efficacy of GPT-4 as a Consultation Assistant.

The role of artificial intelligence (AI) in healthcare is evolving, offering promising avenues for enhancing clinical decision making and patient management. Limited knowledge about lipedema often leads to patients being frequently misdiagnosed with conditions like lymphedema or obesity rather than correctly identifying lipedema. Furthermore, patients with lipedema often present with intricate and extensive medical histories, resulting in significant time consumption during consultations. AI could, therefore, improve the management of these patients. This research investigates the utilization of OpenAI's Generative Pre-Trained Transformer 4 (GPT-4), a sophisticated large language model (LLM), as an assistant in consultations for lipedema patients. Six simulated scenarios were designed to mirror typical patient consultations commonly encountered in a lipedema clinic. GPT-4 was tasked with conducting patient interviews to gather medical histories, presenting its findings, making preliminary diagnoses, and recommending further diagnostic and therapeutic actions. Advanced prompt engineering techniques were employed to refine the efficacy, relevance, and accuracy of GPT-4's responses. A panel of experts in lipedema treatment, using a Likert Scale, evaluated GPT-4's responses across six key criteria. Scoring ranged from 1 (lowest) to 5 (highest), with GPT-4 achieving an average score of 4.24, indicating good reliability and applicability in a clinical setting. This study is one of the initial forays into applying large language models like GPT-4 in specific clinical scenarios, such as lipedema consultations. It demonstrates the potential of AI in supporting clinical practices and emphasizes the continuing importance of human expertise in the medical field, despite ongoing technological advancements.

Even though active recommendation for HPV vaccination has restarted, Japan's rates have not recovered.

Japan has a particularly critical situation surrounding its collapsed HPV vaccination program for preventing HPV-caused cervical cancers, a problem exacerbated by the lack of a national immunization database. We have determined the year-to-year HPV vaccination uptake by Japanese females and analyzed by birth fiscal year (FY) the monthly number of people receiving initial HPV vaccination. Our analysis covers the period from the start of public subsidies in 2010 to September 2023, using data provided by local governments. We calculated the cumulative number of monthly immunizations for those unimmunized as of April (the beginning of each vaccination year). The monthly number of initial HPV vaccinations was highest in August for every FY from FY 2010 to FY 2023; a second vaccination peak tended to occur in March when the vaccination year ended. The highest number of August vaccinations occurred in FY 2011, followed (in order) by 2012, 2021, 2022, 2023, and 2013. In Japan's ongoing catch-up vaccination program for young women, the monthly number of vaccinations increased in August 2022 but then slowed the following year. After FY 2021, the cumulative vaccination coverage of subjects unvaccinated at the beginning of the vaccination year but subsequently covered by routine immunizations was slightly improved. FY 2021 was when the governmental recommendations for HPV vaccination were resumed. More recent vaccination rates are considerably lower than those in FY 2011-2012 when vaccinations were first fully endorsed. Paralyzing HPV vaccination hesitancy, which began in FY 2013, will linger in Japan in FY 2024.

Consensus practice recommendations for management of gastrointestinal dysfunction in Parkinson disease.

BACKGROUND: Gastrointestinal (GI) dysfunction is a common non-motor feature of Parkinson disease (PD). GI symptoms may start years before the onset of motor symptoms and impair quality of life. Robust clinical trial data is lacking to guide screening, diagnosis and treatment of GI dysfunction in PD. OBJECTIVE: To develop consensus statements on screening, diagnosis, and treatment of GI dysfunction in PD. METHODS: The application of a modified Delphi panel allowed for the synthesis of expert opinions into clinical statements. Consensus was predefined as a level of agreement of 100 % for each item. Five virtual Delphi rounds were held. Two movement disorders neurologists reviewed the literature on GI dysfunction in PD and developed draft statements based on the literature review. Draft statements were distributed among the panel that included five movement disorder neurologists and two gastroenterologists, both experts in GI dysmotility and its impact on PD symptoms. All members reviewed the statements and references in advance of the virtual meetings. In the virtual meetings, each statement was discussed, edited, and a vote was conducted. If there was not 100 % consensus, further discussions and modifications ensued until there was consensus. RESULTS: Statements were developed for screening, diagnosis, and treatment of common GI symptoms in PD and were organized by anatomic segments: oral cavity and esophagus, stomach, small intestine, and colon and anorectum. CONCLUSIONS: These consensus recommendations offer a practical framework for the diagnosis and treatment of GI dysfunction in PD.

Concordance of provider chart notation and guideline-based classification of asthma severity.

OBJECTIVE: To evaluate concordance of asthma severity classification via physician chart notation compared with guideline-based criteria in adolescents with diagnosed asthma. METHODS: Of 284 urban primary care and subspecialty clinic patients aged 13-18 years approached through convenience sampling, 203 surveys were completed (RR = 71.5%). We assessed concordance with sensitivity, specificity, and positive predictive values; overall agreement was evaluated with weighted kappa coefficients and McNemar's test. RESULTS: When considering prescribed treatment according to NAEPP guidelines as a gold standard, the sensitivity for chart notation was very good for intermittent (95%) and less for non-intermittent severity ratings (51%, 58%, and 67% for moderate, severe, and mild persistent asthma, respectively). Overall agreement between chart notation and guideline-based asthma criteria ranged from fair-to-good for mild- (k = 0.36), moderate- (k = 0.44), and severe-persistent severity (k = 0.66). Although the agreement for intermittent severity was highest (k = 0.88), it did not significantly differ by between the two classifications (p ≥ 0.05). CONCLUSIONS: Concordance for all non-intermittent asthma severity classifications varied between physician and medication-driven 2007 NAEPP guideline classifications in an ethnically diverse urban adolescent patient sample. Physicians should remain aware of the potential for this discordance and refer to the guidelines to classify and treat adolescents with asthma.