Assessing the reactions of tourist markets to reinstated travel restrictions in the destination during the post-COVID-19 phase.

This study, leveraging search engine data, investigates the dynamics of China's domestic tourism markets in response to the August 2022 epidemic outbreak in Xinjiang. It focuses on understanding the reaction mechanisms of tourist-origin markets during destination crises in the post-pandemic phase. Notably, the research identifies a continuous rise in the potential tourism demand from tourist origin cities, despite the challenges posed by the epidemic. Further analysis uncovers a regional disparity in the growth of tourism demand, primarily influenced by the economic stratification of origin markets. Additionally, the study examines key tourism attractions such as Duku Road, highlighting its resilient competitive system, which consists of distinctive tourism experiences, economically robust tourist origins, diverse tourist markets, and spatial pattern stability driven by economic factors in source cities, illustrating an adaptive response to external challenges such as crises. The findings provide new insights into the dynamics of tourism demand, offering a foundation for developing strategies to bolster destination resilience and competitiveness in times of health crises.

An artificial intelligence-based dental semantic search engine as a reliable tool for dental students and educators.

PURPOSE/OBJECTIVES: This study proposes the utilization of a Natural Language Processing tool to create a semantic search engine for dental education while addressing the increasing concerns of accuracy, bias, and hallucination in outputs generated by AI tools. The paper focuses on developing and evaluating DentQA, a specialized question-answering tool that makes it easy for students to seek information to access information located in handouts or study material distributed by an institution. METHODS: DentQA is structured upon the GPT3.5 language model, utilizing prompt engineering to extract information from external dental documents that experts have verified. Evaluation involves non-human metrics (BLEU scores) and human metrics for the tool's performance, relevance, accuracy, and functionality. RESULTS: Non-human metrics confirm DentQA's linguistic proficiency, achieving a Unigram BLEU score of 0.85. Human metrics reveal DentQA's superiority over GPT3.5 in terms of accuracy (p = 0.00004) and absence of hallucination (p = 0.026). Additional metrics confirmed consistent performance across different question types (X2 (4, N = 200) = 13.0378, p = 0.012). User satisfaction and performance metrics support DentQA's usability and effectiveness, with a response time of 3.5 s and over 70% satisfaction across all evaluated parameters. CONCLUSIONS: The study advocates using a semantic search engine in dental education, mitigating concerns of misinformation and hallucination. By outlining the workflow and the utilization of open-source tools and methods, the study encourages the utilization of similar tools for dental education while underscoring the importance of customizing AI models for dentistry. Further optimizations, testing, and utilization of recent advances can contribute to dental education significantly.

Freely available, online videos to support neurological physiotherapists and students in task-specific training skill acquisition: a scoping review.

BACKGROUND: Videos to support learning of clinical skills are effective; however, little is known about the scope and educational quality of the content of freely available online videos demonstrating task-specific training (TST). This review aimed to determine the extent, characteristics of freely available online videos, and whether the content is suitable to guide skill acquisition of task-specific training for neurological physiotherapists and students. METHODS: A scoping review was conducted. Google video and YouTube were searched in December 2022. Videos that met our eligibility criteria and were explicitly designed for (TST) skill acquisition were included in the report. RESULTS: Ten videos met the inclusion criteria and were difficult to find amongst the range of videos available. Most were presented by physiotherapists or occupational therapists, originated from the USA, featured stroke as the condition of the person being treated, and involved a range of interventions (upper limb, constraint induced movement therapy, balance, bicycling). Most videos were created by universities or private practices and only two used people with a neurological condition as the participant. When the content of videos and their presentation (instruction and/or demonstration), was assessed against each key component of TST (practice structure, specificity, repetition, modification, progression, feedback), five of the videos were rated very suitable and five moderately suitable to guide skill acquisition. Most videos failed to demonstrate and provide instruction on each key component of TST and were missing at least one component, with feedback most frequently omitted. CONCLUSIONS: There are many freely available online videos which could be described as demonstrating TST; very few are suitable to guide skill acquisition. The development of a standardised and validated assessment tool, that is easy to use and assesses the content of TST videos is required to support learners to critically evaluate the educational quality of video content. Guidelines based on sound teaching theory and practice are required to assist creators of online videos to provide suitable resources that meet the learning needs of neurological physiotherapists and students.

The Use of Generative AI for Scientific Literature Searches for Systematic Reviews: ChatGPT and Microsoft Bing AI Performance Evaluation.

Background: A large language model is a type of artificial intelligence (AI) model that opens up great possibilities for health care practice, research, and education, although scholars have emphasized the need to proactively address the issue of unvalidated and inaccurate information regarding its use. One of the best-known large language models is ChatGPT (OpenAI). It is believed to be of great help to medical research, as it facilitates more efficient data set analysis, code generation, and literature review, allowing researchers to focus on experimental design as well as drug discovery and development. Objective: This study aims to explore the potential of ChatGPT as a real-time literature search tool for systematic reviews and clinical decision support systems, to enhance their efficiency and accuracy in health care settings. Methods: The search results of a published systematic review by human experts on the treatment of Peyronie disease were selected as a benchmark, and the literature search formula of the study was applied to ChatGPT and Microsoft Bing AI as a comparison to human researchers. Peyronie disease typically presents with discomfort, curvature, or deformity of the penis in association with palpable plaques and erectile dysfunction. To evaluate the quality of individual studies derived from AI answers, we created a structured rating system based on bibliographic information related to the publications. We classified its answers into 4 grades if the title existed: A, B, C, and F. No grade was given for a fake title or no answer. Results: From ChatGPT, 7 (0.5%) out of 1287 identified studies were directly relevant, whereas Bing AI resulted in 19 (40%) relevant studies out of 48, compared to the human benchmark of 24 studies. In the qualitative evaluation, ChatGPT had 7 grade A, 18 grade B, 167 grade C, and 211 grade F studies, and Bing AI had 19 grade A and 28 grade C studies. Conclusions: This is the first study to compare AI and conventional human systematic review methods as a real-time literature collection tool for evidence-based medicine. The results suggest that the use of ChatGPT as a tool for real-time evidence generation is not yet accurate and feasible. Therefore, researchers should be cautious about using such AI. The limitations of this study using the generative pre-trained transformer model are that the search for research topics was not diverse and that it did not prevent the hallucination of generative AI. However, this study will serve as a standard for future studies by providing an index to verify the reliability and consistency of generative AI from a user's point of view. If the reliability and consistency of AI literature search services are verified, then the use of these technologies will help medical research greatly.

User-centric design of a 3D search interface for protein-ligand complexes.

In this work, we present the frontend of GeoMine and showcase its application, focusing on the new features of its latest version. GeoMine is a search engine for ligand-bound and predicted empty binding sites in the Protein Data Bank. In addition to its basic text-based search functionalities, GeoMine offers a geometric query type for searching binding sites with a specific relative spatial arrangement of chemical features such as heavy atoms and intermolecular interactions. In contrast to a text search that requires simple and easy-to-formulate user input, a 3D input is more complex, and its specification can be challenging for users. GeoMine's new version aims to address this issue from the graphical user interface perspective by introducing an additional visualization concept and a new query template type. In its latest version, GeoMine extends its query-building capabilities primarily through input formulation in 2D. The 2D editor is fully synchronized with GeoMine's 3D editor and provides the same functionality. It enables template-free query generation and template-based query selection directly in 2D pose diagrams. In addition, the query generation with the 3D editor now supports predicted empty binding sites for AlphaFold structures as query templates. GeoMine is freely accessible on the ProteinsPlus web server ( https://proteins.plus ).

Representation of skin carcinomas in public awareness - Awareness worldwide and in Germany.

BACKGROUND AND OBJECTIVES: In recent years, there has been an increase in skin cancer. The aim of this study was therefore to investigate the representation of skin cancer in public awareness worldwide and in Germany, and to determine whether Skin Cancer Awareness Month is represented in the search interests of the Internet-using population in the same way as Breast Cancer Awareness Month worldwide. DATA AND METHODS: In this study, Google Trends data were used to track levels of public awareness for different tumor entities and skin cancer types worldwide and for Germany. RESULTS: The results of this analysis clearly showed a high level of relative public search interest in breast cancer worldwide in the awareness month of October. Worldwide and in Germany, there was a certain increase in search interest and a certain seasonal effect around the May awareness month for skin cancer. For example, the analysis showed a search interest in May and during the summer months in Germany. CONCLUSIONS: It is likely that the population, for example in Germany, may benefit further from an even greater emphasis on the topic of skin cancer.

PS-GO parametric protein search engine.

With the explosive growth of protein-related data, we are confronted with a critical scientific inquiry: How can we effectively retrieve, compare, and profoundly comprehend these protein structures to maximize the utilization of such data resources? PS-GO, a parametric protein search engine, has been specifically designed and developed to maximize the utilization of the rapidly growing volume of protein-related data. This innovative tool addresses the critical need for effective retrieval, comparison, and deep understanding of protein structures. By integrating computational biology, bioinformatics, and data science, PS-GO is capable of managing large-scale data and accurately predicting and comparing protein structures and functions. The engine is built upon the concept of parametric protein design, a computer-aided method that adjusts and optimizes protein structures and sequences to achieve desired biological functions and structural stability. PS-GO utilizes key parameters such as amino acid sequence, side chain angle, and solvent accessibility, which have a significant influence on protein structure and function. Additionally, PS-GO leverages computable parameters, derived computationally, which are crucial for understanding and predicting protein behavior. The development of PS-GO underscores the potential of parametric protein design in a variety of applications, including enhancing enzyme activity, improving antibody affinity, and designing novel functional proteins. This advancement not only provides a robust theoretical foundation for the field of protein engineering and biotechnology but also offers practical guidelines for future progress in this domain.

Scaling up search engine audits: Practical insights for algorithm auditing.

Algorithm audits have increased in recent years due to a growing need to independently assess the performance of automatically curated services that process, filter and rank the large and dynamic amount of information available on the Internet. Among several methodologies to perform such audits, virtual agents stand out because they offer the ability to perform systematic experiments, simulating human behaviour without the associated costs of recruiting participants. Motivated by the importance of research transparency and replicability of results, this article focuses on the challenges of such an approach. It provides methodological details, recommendations, lessons learned and limitations based on our experience of setting up experiments for eight search engines (including main, news, image and video sections) with hundreds of virtual agents placed in different regions. We demonstrate the successful performance of our research infrastructure across multiple data collections, with diverse experimental designs, and point to different changes and strategies that improve the quality of the method. We conclude that virtual agents are a promising venue for monitoring the performance of algorithms across long periods of time, and we hope that this article can serve as a basis for further research in this area.

Search engine optimization and its association with readability and accessibility of diabetic retinopathy websites.

PURPOSE: This study investigated whether websites regarding diabetic retinopathy are readable for patients, and adequately designed to be found by search engines. METHODS: The term "diabetic retinopathy" was queried in the Google search engine. Patient-oriented websites from the first 10 pages were categorized by search result page number and website organization type. Metrics of search engine optimization (SEO) and readability were then calculated. RESULTS: Among the 71 sites meeting inclusion criteria, informational and organizational sites were best optimized for search engines, and informational sites were the most visited. Better optimization as measured by authority score was correlated with lower Flesch Kincaid Grade Level (r = 0.267, P = 0.024). There was a significant increase in Flesch Kincaid Grade Level with successive search result pages (r = 0.275, P = 0.020). Only 2 sites met the 6th grade reading level AMA recommendation by Flesch Kincaid Grade Level; the average reading level was 10.5. There was no significant difference in readability between website categories. CONCLUSION: While the readability of diabetic retinopathy patient information was poor, better readability was correlated to better SEO metrics. While we cannot assess causality, we recommend websites improve their readability, which may increase uptake of their resources.

Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online.

BACKGROUND: The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. OBJECTIVE: The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. METHODS: We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. RESULTS: Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat's and 13.23% (18/136) of Google SGE's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%). CONCLUSIONS: While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations.

Sharing Digital Health Educational Resources in a One-Stop Shop Portal: Tutorial on the Catalog and Index of Digital Health Teaching Resources (CIDHR) Semantic Search Engine.

BACKGROUND: Access to reliable and accurate digital health web-based resources is crucial. However, the lack of dedicated search engines for non-English languages, such as French, is a significant obstacle in this field. Thus, we developed and implemented a multilingual, multiterminology semantic search engine called Catalog and Index of Digital Health Teaching Resources (CIDHR). CIDHR is freely accessible to everyone, with a focus on French-speaking resources. CIDHR has been initiated to provide validated, high-quality content tailored to the specific needs of each user profile, be it students or professionals. OBJECTIVE: This study's primary aim in developing and implementing the CIDHR is to improve knowledge sharing and spreading in digital health and health informatics and expand the health-related educational community, primarily French speaking but also in other languages. We intend to support the continuous development of initial (ie, bachelor level), advanced (ie, master and doctoral levels), and continuing training (ie, professionals and postgraduate levels) in digital health for health and social work fields. The main objective is to describe the development and implementation of CIDHR. The hypothesis guiding this research is that controlled vocabularies dedicated to medical informatics and digital health, such as the Medical Informatics Multilingual Ontology (MIMO) and the concepts structuring the French National Referential on Digital Health (FNRDH), to index digital health teaching and learning resources, are effectively increasing the availability and accessibility of these resources to medical students and other health care professionals. METHODS: First, resource identification is processed by medical librarians from websites and scientific sources preselected and validated by domain experts and surveyed every week. Then, based on MIMO and FNRDH, the educational resources are indexed for each related knowledge domain. The same resources are also tagged with relevant academic and professional experience levels. Afterward, the indexed resources are shared with the digital health teaching and learning community. The last step consists of assessing CIDHR by obtaining informal feedback from users. RESULTS: Resource identification and evaluation processes were executed by a dedicated team of medical librarians, aiming to collect and curate an extensive collection of digital health teaching and learning resources. The resources that successfully passed the evaluation process were promptly included in CIDHR. These resources were diligently indexed (with MIMO and FNRDH) and tagged for the study field and degree level. By October 2023, a total of 371 indexed resources were available on a dedicated portal. CONCLUSIONS: CIDHR is a multilingual digital health education semantic search engine and platform that aims to increase the accessibility of educational resources to the broader health care-related community. It focuses on making resources "findable," "accessible," "interoperable," and "reusable" by using a one-stop shop portal approach. CIDHR has and will have an essential role in increasing digital health literacy.

A Fresh Perspective on Examining Population Emotional Well-Being Trends by Internet Search Engine: An Emerging Composite Anxiety and Depression Index.

Traditional assessments of anxiety and depression face challenges and difficulties when it comes to understanding trends in-group psychological characteristics. As people become more accustomed to expressing their opinions online, location-based online media and cutting-edge algorithms offer new opportunities to identify associations between group sentiment and economic- or healthcare-related variables. Our research provides a novel approach to analyzing emotional well-being trends in a population by focusing on retrieving online information. We used emotionally enriched texts on social media to build the Public Opinion Dictionary (POD). Then, combining POD with the word vector model and search trend, we developed the Composite Anxiety and Depression Index (CADI), which can reflect the mental health level of a region during a specific time period. We utilized the representative external data by CHARLS to validate the effectiveness of CADI, indicating that CADI can serve as a representative indicator of the prevalence of mental disorders. Regression and subgroup analysis are employed to further elucidate the association between public mental health (measured by CADI) with economic development and medical burden. The results of comprehensive regression analysis show that the Import-Export index (-16.272, p < 0.001) and average cost of patients (4.412, p < 0.001) were significantly negatively associated with the CADI, and the sub-models stratificated by GDP showed the same situation. Disposable income (-28.389, p < 0.001) became significant in the subgroup with lower GDP, while the rate of unemployment (2.399, p < 0.001) became significant in the higher subgroup. Our findings suggest that an unfavorable economic development or unbearable medical burden will increase the negative mental health of the public, which was consistent across both the full and subgroup models.

Web-Based Search Volume for HIV Tests and HIV-Testing Preferences During the COVID-19 Pandemic in Japan: Infodemiology Study.

BACKGROUND: Research has found a COVID-19 pandemic-related impact on HIV medical services, including clinic visits, testing, and antiviral therapy initiation in countries including Japan. However, the change in trend for HIV/AIDS testing during the COVID-19 pandemic has not been explored extensively in the Japanese population. OBJECTIVE: This infodemiology study examines the web-based search interest for two types of HIV tests, self-test kits and facility-based tests, before and during the COVID-19 pandemic in Japan. METHODS: The monthly search volume of queried search terms was obtained from Yahoo! JAPAN. Search volumes for the following terms were collected from November 2017 to October 2018: "HIV test," "HIV test kit," and "HIV test health center." The search term "Corona PCR" and the number of new COVID-19 cases by month were used as a control for the search trends. The number of new HIV cases in the corresponding study period was obtained from the AIDS Trend Committee Quarterly Report from the AIDS Prevention Foundation. RESULTS: Compared to the search volume of "corona-PCR," which roughly fluctuated corresponding to the number of new COVID-19 cases in Japan, the search volume of "HIV test" was relatively stable from 2019 to 2022. When we further stratified by the type of HIV test, the respective web-based search interest in HIV self-testing and facility-based testing showed distinct patterns from 2018 to 2022. While the search volume of "HIV test kit" remained stable, that of "HIV test health center" displayed a decreasing trend starting in 2018 and has remained low since the beginning of the COVID-19 pandemic. Around 66%-71% of the search volume of "HIV test kits" was attributable to searches made by male internet users from 2018 to 2022, and the top three contributing age groups were those aged 30-39 (27%-32%), 20-29 (19%-32%), and 40-49 (19%-25%) years. On the other hand, the search volume of "HIV test health centers" by male users decreased from more than 500 from 2018 to 2019 to fewer than 300 from 2020 to 2022. CONCLUSIONS: Our study found a notable decrease in the search volume of "HIV test health center" during the pandemic, while the search volume for HIV self-testing kits remained stable before and during the COVID-19 crisis in Japan. This suggests that the previously reported COVID-19-related decrease in the number of HIV tests mostly likely referred to facility-based testing. This sheds light on the change in HIV-testing preferences in Japan, calling for a more comprehensive application and regulatory acceptance of HIV self-instructed tests.

Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine.

High confidence and reproducibility are still challenges in bottom-up mass spectrometric N-glycopeptide identification. The collision energy used in the MS/MS measurements and the database search engine used to identify the species are perhaps the two most decisive factors. We investigated how the structural features of N-glycopeptides and the choice of the search engine influence the optimal collision energy, delivering the highest identification confidence. We carried out LC-MS/MS measurements using a series of collision energies on a large set of N-glycopeptides with both the glycan and peptide part varied and studied the behavior of Byonic, pGlyco, and GlycoQuest scores. We found that search engines show a range of behavior between peptide-centric and glycan-centric, which manifests itself already in the dependence of optimal collision energy on m/z. Using classical statistical and machine learning methods, we revealed that peptide hydrophobicity, glycan and peptide masses, and the number of mobile protons also have significant and search-engine-dependent influence, as opposed to a series of other parameters we probed. We envisioned an MS/MS workflow making a smart collision energy choice based on online available features such as the hydrophobicity (described by retention time) and glycan mass (potentially available from a scout MS/MS). Our assessment suggests that this workflow can lead to a significant gain (up to 100%) in the identification confidence, particularly for low-scoring hits close to the filtering limit, which has the potential to enhance reproducibility of N-glycopeptide analyses. Data are available via MassIVE (MSV000093110).

Mind the Gap: A Questionnaire on the Distance between Diagnostic Advances and Clinical Practice in Skin Cancer Treatment.

Background and Objectives: Significant progress has been made in skin cancer diagnosis, with a surge in available technologies in recent years. Despite this, the practical application and integration of these technologies in dermatology and plastic surgery remain uneven. Materials and Methods: A comprehensive 20-question survey was designed and distributed using online survey administration software (Google Forms, 2018, Google, Mountain View, CA, USA) from June 2023 to September 2023. The survey aimed to assess the knowledge and utilization of dermatologic diagnostic advancements among plastic surgeons in various European countries. Results: Data were obtained from 29 plastic surgeons across nine European countries, revealing a notable gap between diagnostic technologies and their routine use in surgical practice. The gap for some technologies was both cognitive and applicative; for electrical impedance spectroscopy (EIS) and multispectral imaging, only 6.9% of the sample knew of the technologies and no surgeons in the sample used them. In the case of other technologies, such as high-frequency ultrasound (HFUS), 72.4% of the sample knew about them but only 34.5% used them, highlighting a more significant application problem. Conclusions: Spotlighting this discrepancy provides a valuable foundation for initiating collaborative efforts between units and facilitating knowledge exchange among diverse specialists. This, in turn, contributes to advancing clinical practice by integrating the innovative opportunities presented by ongoing research.

Celebrities influence on rheumatic diseases interest: a Google Trends analysis.

Celebrity-related events have influenced public interest in diseases like cancer, but their impact on rheumatic diseases is poorly investigated. We aimed to investigate whether celebrity-related events may account for atypical interest among Google users in rheumatic diseases. We used Google Trends to generate the relative search volume of 24 adult rheumatic diseases. We visually analyzed global time trends and recorded all dates with unusual spikes of interest. Finally, we used the Google search engine to detect media news related to rheumatic disease that may explain the spikes. The majority of atypical spikes in global interest were attributable to celebrity-related events, such as diagnosis, flare, or death due to rheumatic disease. Examples include Venus Williams with Sjögren's syndrome, Lady Gaga with fibromyalgia, Selena Gomez with lupus, Phil Mickelson with psoriatic arthritis, and Ashton Kutcher with vasculitis. Celebrity-related events may have a substantial influence on global interest in rheumatic diseases among Google users. These findings suggest that leveraging the attention generated by celebrities can be a powerful tool in raising awareness and promoting research efforts for rheumatic diseases. Future studies could leverage Google Trends to gauge the influence of celebrity events or health campaigns on rheumatic disease awareness.

Assessment of internet-based information on statin therapy.

AIMS: The use of statin therapy is deemed to be controversial by mainstream media. Patients increasingly source medical information from the internet, and the use of statins is no exception. This study aims to determine the quality and educational content of statin-focused information on the internet and YouTube. METHODS AND RESULTS: 'Statin' was searched on Google, Yahoo!, Bing, and YouTube. The first 50 results obtained from each search engine and the first 20 YouTube videos were screened by two assessors. Websites were assessed using the Flesch Reading Ease (FRE) score, University of Michigan Consumer Health Website Evaluation Checklist, and a customized scoring system evaluating statin-focused content for quality. Videos were scored using the Journal of the American Medical Association (JAMA) benchmark criteria, Global Quality Score (GQS), and the customized scoring system. Websites scored a median FRE score of 57.5 [interquartile range (IQR) 52.1-62.3], median Michigan score of 36 (IQR 32-41.5), and median content score of 5 (IQR 3.75-7). Good interobserver agreement was demonstrated [Michigan score interobserver coefficient correlation (ICC) = 0.968; content score ICC = 0.944]. Videos scored a median JAMA score of 2, median GQS score of 2.5, and median content score of 2.5. Good interobserver agreement was demonstrated (JAMA ICC = 0.746; GQS ICC = 0.874; content score ICC = 0.946). CONCLUSION: Quality and readability of statin-focused online information are poor. Healthcare professionals should be aware of the limitations of the current available sources and design online resources that are accurate and patient-friendly.

Identifying amyotrophic lateral sclerosis through interactions with an internet search engine.

INTRODUCTION/AIMS: Amyotrophic lateral sclerosis (ALS), a motor neuron disease, remains a clinical diagnosis with an average time from onset of symptoms to diagnosis of about 1 year. Herein we examine the possibility that interactions with an internet search engine can identify people with ALS. METHODS: We identified 285 anonymous Bing users whose queries indicated that they had been diagnosed with ALS and matched them to: (1) 3276 control users; and (2) 1814 users whose searches indicated they had ALS disease mimics. We tested whether the ALS group could be distinguished from controls and disease mimics based on search engine query data. Finally, we conducted a prospective validation from participants who provided access to their Bing search data. RESULTS: The model distinguished between the ALS group and controls with an area under the curve (AUC) of 0.81. Model scores for the ALS group differed from the disease mimics group (rank sum test, p < .05 with Bonferroni correction). Mild cognitive impairment could not be distinguished from ALS (p > .05). In the prospective analysis, the model reached an AUC of 0.74. DISCUSSION: Our results suggest that interactions with search engines should be further studied to understand the potential to act as a tool to assist in screening for ALS and to reduce diagnostic delay.

Inefficacious drugs against covid-19: analysis of sales, tweets, and search engines / Medicamentos ineficazes contra covid-19: análise de vendas, tweets e mecanismos de busca

ABSTRACT OBJECTIVE Assess the correlation between the sales of two drugs with no proven efficacy against covid-19, ivermectin and chloroquine, and other relevant variables, such as Google® searches, number of tweets related to these drugs, number of cases and deaths resulting from covid-19. METHODS The methodology adopted in this study has four stages: data collection, data processing, exploratory data analysis, and correlation analysis. Spearman's method was used to obtain cross-correlations between each pair of variables. RESULTS The results show similar behaviors between variables. Peaks occurred in the same or near periods. The exploratory data analysis showed shortage of chloroquine in the period corresponding to the beginning of advertising for the application of these drugs against covid-19. Both drugs showed a high and statistically significant correlation with the other variables. Also, some of them showed a higher correlation with drug sales when we employed a one-month lag. In the case of chloroquine, this was observed for the number of deaths. In the case of ivermectin, this was observed for the number of tweets, cases, and deaths. CONCLUSIONS The results contribute to decision making in crisis management by governments, industries, and stores. In times of crisis, as observed during the covid-19 pandemic, some variables can help sales forecasting, especially Google® and tweets, which provide a real-time analysis of the situation. Monitoring social media platforms and search engines would allow the determination of drug use by the population and better prediction of potential peaks in the demand for these drugs.

RESUMO OBJETIVO Investigar a correlação entre as vendas de dois medicamentos sem eficácia comprovada no tratamento de covid-19, ivermectina e cloroquina, e outras variáveis relevantes: pesquisas no Google®, número de tweets relacionados aos medicamentos, casos e óbitos decorrentes da covid-19. MÉTODOS A metodologia adotada neste estudo se divide em quatro partes: coleta de dados; processamento dos dados; análise exploratória; e análise de correlação. Foi utilizado o método de Spearman para obter as correlações cruzadas entre cada par de variáveis. RESULTADOS Os resultados mostram similaridade entre os comportamentos das variáveis. Os picos ocorreram em períodos iguais ou próximos. A análise exploratória dos dados apontou que houve falta de cloroquina no período correspondente ao início das divulgações sobre a aplicação desses medicamentos para o tratamento da covid-19. Ambos os medicamentos apresentaram correlação alta e estatisticamente significativa com as demais variáveis analisadas. Também foi observado que algumas delas apresentaram maior correlação com as vendas de medicamentos quando assumiram defasagem temporal de um mês. No caso da cloroquina, isso ocorreu com a variável óbitos. No caso da ivermectina, ocorreu com as variáveis número de tweets, casos e óbitos. CONCLUSÕES Os resultados observados contribuem para a tomada de decisão durante a gestão de crises por parte de governo, indústrias e comércios. Em momentos de crises, como observado durante a pandemia, as variáveis mostraram que são capazes de auxiliar na previsão de vendas, em especial o Google® e os tweets, que proporcionam uma análise em tempo real da situação. Acompanhar as redes sociais e mecanismos de busca permitiria detecção de uso pela população e melhor previsão de potenciais picos de demanda desses medicamentos.