Multi-level sequence denoising with cross-signal contrastive learning for sequential recommendation.

Sequential recommender systems (SRSs) aim to suggest next item for a user based on her historical interaction sequences. Recently, many research efforts have been devoted to attenuate the influence of noisy items in sequences by either assigning them with lower attention weights or discarding them directly. The major limitation of these methods is that the former would still prone to overfit noisy items while the latter may overlook informative items. To the end, in this paper, we propose a novel model named Multi-level Sequence Denoising with Cross-signal Contrastive Learning (MSDCCL) for sequential recommendation. To be specific, we first introduce a target-aware user interest extractor to simultaneously capture users' long and short term interest with the guidance of target items. Then, we develop a multi-level sequence denoising module to alleviate the impact of noisy items by employing both soft and hard signal denoising strategies. Additionally, we extend existing curriculum learning by simulating the learning pattern of human beings. It is worth noting that our proposed model can be seamlessly integrated with a majority of existing recommendation models and significantly boost their effectiveness. Experimental studies on five public datasets are conducted and the results demonstrate that the proposed MSDCCL is superior to the state-of-the-art baselines. The source code is publicly available at https://github.com/lalunex/MSDCCL/tree/main.

Application of KL distance-based intelligent recommendation method to fund recommendation for users with investment behavior in Asia Region.

Over the past decade, the spectacular growth of many economies in Asia has amazed the economics profession and Asian people's quality of life has been significantly improved. Therefore, people's savings are increasing. The booming financial market attracts more people to invest in funds for its relatively low risk and high returns. This research first analyzed the widely used recommendation algorithms. Then it adopted the K-medoids clustering algorithm based on KL divergence to make recommendations. The recommendations were also based on the user-item rating matrix. First, the improved Kullback-Leibler distance was used to calculate the distance and similarity between the funds. Next, the data set of n funds were split by the K-medoids clustering technique into k clusters to obtain funds that were similar to the target fund. Finally, the prediction accuracy of the KL-KM recommendation algorithm was compared with those of traditional recommendation algorithms. When the number of recommendations changed from 1 to 5, the average minimum KL distance varied from 0.16, 0.22, 0.29, 0.31, to 0.39. The average KL similarity is 0.85, 0.83, 0.79, 0.74, and 0.72, respectively, and the average absolute error is 0.83, 0.82, 0.82, 0.83, and 0.82, respectively. The root mean square error (RMSE) of the proposed algorithm is 0.93, 0.94, 0.93, 0.94 and 0.94, respectively. Therefore, the proposed recommendation model has a better recommendation performance than existing models to meet users' demands. This algorithm generates recommendations of similar funds based on customers' purchase history. Moreover, principal component analysis is used to simplify the large data set of indicator values into a smaller set while still maintaining significant patterns and trends, thereby improving the accuracy and reducing the complexity of the model.

Multi-granularity contrastive learning model for next POI recommendation.

Next Point-of-Interest (POI) recommendation aims to predict the next POI for users from their historical activities. Existing methods typically rely on location-level POI check-in trajectories to explore user sequential transition patterns, which suffer from the severe check-in data sparsity issue. However, taking into account region-level and category-level POI sequences can help address this issue. Moreover, collaborative information between different granularities of POI sequences is not well utilized, which can facilitate mutual enhancement and benefit to augment user preference learning. To address these challenges, we propose multi-granularity contrastive learning (MGCL) for next POI recommendation, which utilizes multi-granularity representation and contrastive learning to improve the next POI recommendation performance. Specifically, location-level POI graph, category-level, and region-level sequences are first constructed. Then, we use graph convolutional networks on POI graph to extract cross-user sequential transition patterns. Furthermore, self-attention networks are used to learn individual user sequential transition patterns for each granularity level. To capture the collaborative signals between multi-granularity, we apply the contrastive learning approach. Finally, we jointly train the recommendation and contrastive learning tasks. Extensive experiments demonstrate that MGCL is more effective than state-of-the-art methods.

Flavonoids intake and weight-adjusted waist index: insights from a cross-sectional study of NHANES.

This study conducted data on 15,446 adults to explore the impact of flavonoids on weight-adjusted waist index (WWI). This was a nationwide cross-sectional study among US adults aged 20 years or older. Dietary intake of flavonoids was assessed through 24-h recall questionnaire. WWI was calculated by dividing waist circumference (WC) by the square root of weight. We utilized weighted generalized linear regression to evaluate the association between flavonoids intake and WWI, and restricted cubic splines (RCS) to explore potential non-linear relationships. Our findings indicated that individuals with lower WWI experienced a notable increase in their consumption of total flavonoids, flavanones, flavones, flavan-3-ols, and anthocyanidins intake (ß (95% CI); -0.05(-0.09, -0.01); -0.07(-0.13, 0.00); -0.07(-0.11, -0.02); -0.06(-0.11, 0.00); -0.13(-0.18, -0.08), respectively), with the exception of flavonols and isoflavones. Additionally, consumption of total flavonoids, flavonols, flavanones, isoflavones, and flavan-3-ols had a non-linear relationship with WWI (all P for non-linearity < 0.05). Furthermore, the effect of total flavonoids on WWI varied in race (P for interaction = 0.011), gender (P for interaction = 0.038), and poverty status (P for interaction = 0.002). These findings suggested that increase the intake of flavonoids might prevent abdominal obesity, but further prospective studies are requested before dietary recommendation.

Assessing GPT-4's Performance in Delivering Medical Advice: Comparative Analysis With Human Experts.

Background: Accurate medical advice is paramount in ensuring optimal patient care, and misinformation can lead to misguided decisions with potentially detrimental health outcomes. The emergence of large language models (LLMs) such as OpenAI's GPT-4 has spurred interest in their potential health care applications, particularly in automated medical consultation. Yet, rigorous investigations comparing their performance to human experts remain sparse. Objective: This study aims to compare the medical accuracy of GPT-4 with human experts in providing medical advice using real-world user-generated queries, with a specific focus on cardiology. It also sought to analyze the performance of GPT-4 and human experts in specific question categories, including drug or medication information and preliminary diagnoses. Methods: We collected 251 pairs of cardiology-specific questions from general users and answers from human experts via an internet portal. GPT-4 was tasked with generating responses to the same questions. Three independent cardiologists (SL, JHK, and JJC) evaluated the answers provided by both human experts and GPT-4. Using a computer interface, each evaluator compared the pairs and determined which answer was superior, and they quantitatively measured the clarity and complexity of the questions as well as the accuracy and appropriateness of the responses, applying a 3-tiered grading scale (low, medium, and high). Furthermore, a linguistic analysis was conducted to compare the length and vocabulary diversity of the responses using word count and type-token ratio. Results: GPT-4 and human experts displayed comparable efficacy in medical accuracy ("GPT-4 is better" at 132/251, 52.6% vs "Human expert is better" at 119/251, 47.4%). In accuracy level categorization, humans had more high-accuracy responses than GPT-4 (50/237, 21.1% vs 30/238, 12.6%) but also a greater proportion of low-accuracy responses (11/237, 4.6% vs 1/238, 0.4%; P=.001). GPT-4 responses were generally longer and used a less diverse vocabulary than those of human experts, potentially enhancing their comprehensibility for general users (sentence count: mean 10.9, SD 4.2 vs mean 5.9, SD 3.7; P<.001; type-token ratio: mean 0.69, SD 0.07 vs mean 0.79, SD 0.09; P<.001). Nevertheless, human experts outperformed GPT-4 in specific question categories, notably those related to drug or medication information and preliminary diagnoses. These findings highlight the limitations of GPT-4 in providing advice based on clinical experience. Conclusions: GPT-4 has shown promising potential in automated medical consultation, with comparable medical accuracy to human experts. However, challenges remain particularly in the realm of nuanced clinical judgment. Future improvements in LLMs may require the integration of specific clinical reasoning pathways and regulatory oversight for safe use. Further research is needed to understand the full potential of LLMs across various medical specialties and conditions.

Letter of recommendation characteristics associated with interview offer to a vascular surgery residency program.

BACKGROUND: Letters of recommendation (LORs) are considered by program directors (PDs) to be an integral part of the residency application. With the conversion of USMLE Step 1 to a binary pass/fail outcome, LORs will likely have higher important in the application process moving forward. However, their utility in securing an interview for a particular applicant remains undetermined. This study aims to identify the applicant and LOR characteristics associated with an interview invitation. METHODS: Letter writer (n=977) characteristics were abstracted from applications (n=264) to an individual integrated vascular surgery residency program over 2 application cycles. A validated text analysis program, Linguistic Inquiry and Word Count, was used to characterize LOR content. Applicant, letter writer, and LOR characteristics associated with an interview invitation was determined using multivariable analysis. RESULTS: Letter writers were 70.9% vascular surgeons (VS), 23.7% PDs, and 45.4% professors. Applicants offered an interview were more likely to come from a top 50 medical school (35.2% vs 25.8%, p=0.013) and an institution with a home vascular program (45.5% vs 34.1%, p=0.006). Alpha Omega Alpha membership was significantly associated with interview offer (28.4%, p<0.001). A greater proportion of letters from VS was associated with an interview offer (p <0.001) compared with letter writers of other specialties. One or more PD letters was significantly associated with an interview offer (79.55% vs 20.45%, p=0.008), whereas number of letters from APDs was not significantly associated with interview offer. Letters written by away institution faculty were significantly associated with interview offer (75%, p<0.001), whereas nonclinical letters were not. Presence of one or more letters from a chair (57.95% vs 42.05%, p=0.015) or chief (67.05% vs 32.95%, p=0.028) was significantly associated with interview offer. Letters for applicants offered an interview had more references to research and teaching, which were more common in letters written by VS. Letters written by PDs were more likely to use assertive, advertising language in favor of applicants. There were no significant applicant, letter writer, or LOR characteristics associated with a top 20 rank. CONCLUSION: Successful applicants were more likely to have LORs written by VS, PDs, and those of higher academic rank with references to research and teaching.

Evaluating Large Language Models for Automated Reporting and Data Systems Categorization: Cross-Sectional Study.

BACKGROUND: Large language models show promise for improving radiology workflows, but their performance on structured radiological tasks such as Reporting and Data Systems (RADS) categorization remains unexplored. OBJECTIVE: This study aims to evaluate 3 large language model chatbots-Claude-2, GPT-3.5, and GPT-4-on assigning RADS categories to radiology reports and assess the impact of different prompting strategies. METHODS: This cross-sectional study compared 3 chatbots using 30 radiology reports (10 per RADS criteria), using a 3-level prompting strategy: zero-shot, few-shot, and guideline PDF-informed prompts. The cases were grounded in Liver Imaging Reporting & Data System (LI-RADS) version 2018, Lung CT (computed tomography) Screening Reporting & Data System (Lung-RADS) version 2022, and Ovarian-Adnexal Reporting & Data System (O-RADS) magnetic resonance imaging, meticulously prepared by board-certified radiologists. Each report underwent 6 assessments. Two blinded reviewers assessed the chatbots' response at patient-level RADS categorization and overall ratings. The agreement across repetitions was assessed using Fleiss κ. RESULTS: Claude-2 achieved the highest accuracy in overall ratings with few-shot prompts and guideline PDFs (prompt-2), attaining 57% (17/30) average accuracy over 6 runs and 50% (15/30) accuracy with k-pass voting. Without prompt engineering, all chatbots performed poorly. The introduction of a structured exemplar prompt (prompt-1) increased the accuracy of overall ratings for all chatbots. Providing prompt-2 further improved Claude-2's performance, an enhancement not replicated by GPT-4. The interrun agreement was substantial for Claude-2 (k=0.66 for overall rating and k=0.69 for RADS categorization), fair for GPT-4 (k=0.39 for both), and fair for GPT-3.5 (k=0.21 for overall rating and k=0.39 for RADS categorization). All chatbots showed significantly higher accuracy with LI-RADS version 2018 than with Lung-RADS version 2022 and O-RADS (P<.05); with prompt-2, Claude-2 achieved the highest overall rating accuracy of 75% (45/60) in LI-RADS version 2018. CONCLUSIONS: When equipped with structured prompts and guideline PDFs, Claude-2 demonstrated potential in assigning RADS categories to radiology cases according to established criteria such as LI-RADS version 2018. However, the current generation of chatbots lags in accurately categorizing cases based on more recent RADS criteria.

A novel clustered-based detection method for shilling attack in private environments.

The topic of privacy-preserving collaborative filtering is gaining more and more attention. Nevertheless, privacy-preserving collaborative filtering techniques are vulnerable to shilling or profile injection assaults. Hence, it is crucial to identify counterfeit profiles in order to achieve total success. Various techniques have been devised to identify and prevent intrusion patterns from infiltrating the system. Nevertheless, these strategies are specifically designed for collaborative filtering algorithms that do not prioritize privacy. There is a scarcity of research on identifying shilling attacks in recommender systems that prioritize privacy. This work presents a novel technique for identifying shilling assaults in privacy-preserving collaborative filtering systems. We employ an ant colony clustering detection method to effectively identify and eliminate fake profiles that are created by six widely recognized shilling attacks on compromised data. The objective of the study is to categorize the fraudulent profiles into a specific cluster and separate this cluster from the system. Empirical experiments are conducted with actual data. The empirical findings demonstrate that the strategy derived from the study effectively eliminates fraudulent profiles in privacy-preserving collaborative filtering.

Contrastive cross-domain sequential recommendation via emphasized intention features.

The objective of cross-domain sequential recommendation is to forecast upcoming interactions by leveraging past interactions across diverse domains. Most methods aim to utilize single-domain and cross-domain information as much as possible for personalized preference extraction and effective integration. However, on one hand, most models ignore that cross-domain information is composed of multiple single-domains when generating representations. They still treat cross-domain information the same way as single-domain information, resulting in noisy representation generation. Only by imposing certain constraints on cross-domain information during representation generation can subsequent models minimize interference when considering user preferences. On the other hand, some methods neglect the joint consideration of users' long-term and short-term preferences and reduce the weight of cross-domain user preferences to minimize noise interference. To better consider the mutual promotion of cross-domain and single-domains factors, we propose a novel model (C2DREIF) that utilizes Gaussian graph encoders to handle information, effectively constraining the correlation of information and capturing useful contextual information more accurately. It also employs a Top-down transformer to accurately extract user intents within each domain, taking into account the user's long-term and short-term preferences. Additionally, entropy regularized is applied to enhance contrastive learning and mitigate the impact of randomness caused by negative sample composition.

A/B Testing of User Enrollment Forms to Enhance Diversity in the Biomedical Workforce via the National Research Mentoring Network: User-Centered Design Case Study.

Background: The National Research Mentoring Network (NRMN) is a National Institutes of Health-funded program for diversifying the science, technology, engineering, math, and medicine research workforce through the provision of mentoring, networking, and professional development resources. The NRMN provides mentoring resources to members through its online platform-MyNRMN. Objective: MyNRMN helps members build a network of mentors. Our goal was to expand enrollment and mentoring connections, especially among those who have been historically underrepresented in biomedical training and the biomedical workforce. Methods: To improve the ease of enrollment, we implemented the split testing of iterations of our user interface for platform registration. To increase mentoring connections, we developed multiple features that facilitate connecting via different pathways. Results: Our improved user interface yielded significantly higher rates of completed registrations (P<.001). Our analysis showed improvement in completed enrollments that used the version 1 form when compared to those that used the legacy form (odds ratio 1.52, 95% CI 1.30-1.78). The version 2 form, with its simplified, 1-step process and fewer required fields, outperformed the legacy form (odds ratio 2.18, 95% CI 1.90-2.50). By improving the enrollment form, the rate of MyNRMN enrollment completion increased from 57.3% (784/1368) with the legacy form to 74.5% (2016/2706) with the version 2 form. Our newly developed features delivered an increase in connections between members. Conclusions: Our technical efforts expanded MyNRMN's membership base and increased connections between members. Other platform development teams can learn from these efforts to increase enrollment among underrepresented groups and foster continuing, successful engagement.

A fusion approach using GIS, green area detection, weather API and GPT for satellite image based fertile land discovery and crop suitability.

Proper utilization of agricultural land is a big challenge as they often laid over as waste lands. Farming is a significant occupation in any country and improving it further by promoting more farming opportunities will take the country towards making a huge leap forward. The issue in achieving this would be the lack of knowledge of cultivable land for food crops. The objective of this work is to utilize modern computer vision technology to identify and map cultivable land for agricultural needs. With increasing population and demand for food, improving the farming sector is crucial. However, the challenge lies in the lack of suitable land for food crops cultivation. To tackle this issue, we propose to use sophisticated image processing techniques on satellite images of the land to determine the regions that are capable of growing food crops. The solution architecture includes enhancement of satellite imagery using sophisticated pan sharpening techniques, notably the Brovey transformation, aiming to transform dull satellite images into sharper versions, thereby improving the overall quality and interpretability of the visual data. Making use of the weather data on the location observed and taking into factors like the soil moisture, weather, humidity, wind, sunlight times and so on, this data is fed into a generative pre-trained transformer model which makes use of it and gives a set of crops that are suitable to be grown on this piece of land under the said conditions. The results obtained by the proposed fusion approach is compared with the dataset provided by the government for different states in India and the performance was measured. We achieved an accuracy of 80% considering the crop suggested by our model and the predominant crop of the region. Also, the classification report detailing the performance of the proposed model is presented.

Comparison of the recommendation of international autoimmune hepatitis pathology group 2022 and the simplified criteria for autoimmune hepatitis 2008: A preliminary study.

Background and Aim: The histological diagnosis of autoimmune hepatitis (AIH) is challenging. A new consensus recommendation was provided by the International AIH Pathology Group to address the problems in the histological diagnosis. The purpose of this study is to compare the 2008 'simplified' criteria for AIH with the 'consensus recommendation' of 2022 in terms of diagnostic sensitivity. Materials and Methods: A retrospective analysis was conducted on pathological specimens of patients diagnosed with Autoimmune Hepatitis (AIH) between 2010 and 2022. Out of 188 patients enlisted, 88 were selected based on exclusion criteria. The specimens were examined by two experienced hepatopathologists and a resident pathologist. All specimens were analyzed using both the "simplified" criteria and the new consensus recommendations. Results: Out of a total of 78 patients, the 2022 consensus recommendations raised the diagnostic category of 16 patients (20.5%) to a higher level. Six patients who were previously diagnosed as "atypical" were now considered "possible AIH", while 10 patients with a "compatible" diagnosis were elevated to "likely AIH" category. No patients were found to fall into a lower diagnostic category according to the new recommendations. A significant difference in diagnostic sensitivity was observed between the 2008 criteria and the 2022 consensus report (p<0.001). Conclusion: The 2022 consensus recommendation may be more sensitive in the diagnosis of AIH in comparison to the 2008 'simplified' histological criteria. More studies are needed both for the validation of the sensitivity of the new consensus recommendation and for the determination of the specificity.

Gender-based linguistic differences in letters of recommendation for rhinology fellowship over time: A dual-institutional follow-up study using natural language processing and deep learning.

KEY POINTS: This follow-up dual-institutional and longitudinal study further evaluated for underlying gender biases in LORs for rhinology fellowship. Explicit and implicit linguistic gender bias was found, heavily favoring male applicants.

Zinc Toxicity: Understanding the Limits.

Zinc, a vital trace element, holds significant importance in numerous physiological processes within the body. It participates in over 300 enzymatic reactions, metabolic functions, regulation of gene expression, apoptosis and immune modulation, thereby demonstrating its essential role in maintaining overall health and well-being. While zinc deficiency is associated with significant health risks, an excess of this trace element can also lead to harmful effects. According to the World Health Organization (WHO), 6.7 to 15 mg per day are referred to be the dietary reference value. An excess of the recommended daily intake may result in symptoms such as anemia, neutropenia and zinc-induced copper deficiency. The European Food Safety Authority (EFSA) defines the tolerable upper intake level (UL) as 25 mg per day, whereas the Food and Drug Administration (FDA) allows 40 mg per day. This review will summarize the current knowledge regarding the calculation of UL and other health risks associated with zinc. For example, zinc intake is not limited to oral consumption; other routes, such as inhalation or topical application, may also pose risks of zinc intoxication.

Nutrition-Related Knowledge Graph Neural Network for Food Recommendation.

Food recommendation systems are becoming increasingly vital in modern society, given the fast-paced lifestyle and diverse dietary habits. Existing research and implemented solutions often rely on user preferences and past behaviors for recommendations, which poses significant issues. Firstly, this approach inadequately considers the nutritional content of foods, potentially leading to recommendations that are overly homogeneous and lacking in diversity. Secondly, it may result in repetitive suggestions of the same types of foods, thereby encouraging users to develop unhealthy dietary habits that could adversely affect their overall health. To address this issue, we introduce a novel nutrition-related knowledge graph (NRKG) method based on graph convolutional networks (GCNs). This method not only enhances users' ability to select appropriate foods but also encourages the development of healthy eating habits, thereby contributing to overall public health. The NRKG method comprises two key components: user nutrition-related food preferences and recipe nutrition components. The first component gathers nutritional information from recipes that users show interest in and synthesizes these data for user reference. The second component connects recipes with similar nutritional profiles, forming a complex heterogeneous graph structure. By learning from this graph, the NRKG method integrates user preferences with nutritional data, resulting in more accurate and personalized food recommendations. We evaluated the NRKG method against six baseline methods using real-world food datasets. In the 100% dataset, the five metrics exceeded the performance of the best baseline method by 2.8%, 5.9%, 1.5%, 9.7%, and 6.0%, respectively. The results indicate that our NRKG method significantly outperforms the baseline methods, including FeaStNet, DeepGCN, GraphSAGE, GAT, UniMP, and GATv2, demonstrating its superiority and effectiveness in promoting healthier and more diverse eating habits. Unlike these baseline methods, which primarily focus on hierarchical information propagation, our NRKG method offers a more comprehensive approach by integrating the nutritional information of recipes with user preferences.

Risk Stratification, Intention to Fast, and Outcomes of Fasting During Ramadan in People With Diabetes Presenting to a Tertiary Care Hospital.

OBJECTIVE: The objectives of this study were to evaluate the stratification of people with diabetes mellitus (DM) based on the International Diabetes Federation-Diabetes and Ramadan 2021 risk calculator into different risk categories and assess their intentions to fast and outcomes of fasting during the holy month of Ramadan. METHODS: This was a 3-month prospective study that was performed from February 9, 2023, to May 6, 2023 (6 weeks before Ramadan until 6 weeks after Ramadan), at a tertiary care hospital in Pakistan. Data regarding glycemic control, characteristics and complications of diabetes, comorbidities, and the various factors that influence fasting were gathered from patients of either sex aged 18 to 80 years with any type of diabetes. The International Diabetes Federation-Diabetes and Ramadan 2021 risk calculation and recommendation were made accordingly for each patient. RESULTS: This study comprised of 460 participants with DM, with 174 males (37.8%) and 286 females (62.2%). The risk categorization showed that 209 (45.4%), 107 (23.3%), and 144 (31.3%) of the participants were in the low-, moderate-, and high-risk categories, respectively. Of the 144 high-risk patients who fasted, 57.9% experienced hypoglycemia (P <.0001). The recommendation of fasting showed statistically significant differences with risk categories, intention to fast, hypoglycemia, type of DM, duration of DM, level of glycemic control, and days of fasting (P <.001). CONCLUSION: A statistically significant number of participants in the high-risk group who fasted experienced complications. This reiterates the importance of rigorous adherence to the medical recommendations.

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.

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.

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).

A cross-sectional study evaluating the exercise discussion with oncologist during cancer consultation: the CONNECT study.

BACKGROUND: Evidence demonstrates that physical exercise confers several psycho-physical benefits on patients with cancer. This study aims to investigate the role of oncologists in exercise promotion. PATIENTS AND METHODS: A multicenter, cross-sectional study was conducted by distributing an anonymous, self-administered questionnaire to patients with cancer. The questionnaire enclosed demographic, health, and exercise variables. The exercise-related questions included in the study used the Godin-Shephard Leisure-Time Physical Activity Questionnaire to measure the amount of physical exercise. In addition, the survey gathered information on whether exercise was discussed with patients, and whether oncologists followed the assess, advise, reinforce, and refer (AARR) process regarding exercise. The survey also asked if patients preferred that exercise be discussed during their consultations. Descriptive statistics and logistic regression were applied. RESULTS: With a response rate of 75%, a total of 549 patients completed the survey. Regarding the exercise discussion, 38% of patients stated that their oncologist initiated an exercise discussion, 14% started the discussion themselves, and 48% said that the issue was not considered. Overall, 35% of patients reported that the oncologist assessed their exercise level, 22% and 42% received advice or reinforcement to increase their exercise, respectively, and 10% were referred to a dedicated service. Regarding preferences, 72% of patients thought that the oncologists should initiate an exercise discussion, 2% that only patients should start the discussion, and 26% thought that the issue should not be discussed. Similarly, 74% of patients are willing to receive the exercise assessment, 59% and 75% the advice and reinforcement to increase their exercise, and 46% to be referred to an exercise service. CONCLUSIONS: Although exercise promotion rates are low, patients are willing to receive exercise information. Dedicated strategies should be developed to support oncologists in promoting exercise to their patients.