bims-librar 2024-09-15 papers

bims-librar

Biomed News

on Biomedical librarianship

Issue of 2024‒09‒15
24 papers selected by
Thomas Krichel, Open Library Society

The medical librarian's roles in the patient education process: A scoping review protocol.
Assessing the Readiness of Rural Public Librarians to Implement Public Health Programs.
VADIS: A Visual Analytics Pipeline for Dynamic Document Representation and Information-Seeking.
Searching for studies: A guide to information retrieval for Campbell systematic reviews.
PUREsuggest: Citation-based Literature Search and Visual Exploration with Keyword-controlled Rankings.
Air mycobiome in the National Library of Greece following relocation to novel premises.
How to set up and run a Bad Bugs Bookclub group.
Quality Measurement of Consumer Health Questions: Content and Language Perspectives.
Content and readability of patient educational materials about neuromodulation for childhood movement disorders.
Assessing the quality and readability of online patient information: ENT UK patient information e-leaflets vs responses by a Generative Artificial Intelligence.
Assessing the readability, reliability, and quality of artificial intelligence chatbot responses to the 100 most searched queries about cardiopulmonary resuscitation: An observational study.
Search for medical information for chronic rhinosinusitis through an artificial intelligence ChatBot.
Accuracy assessment of ChatGPT responses to frequently asked questions regarding anterior cruciate ligament surgery.
Prompt matters: evaluation of large language model chatbot responses related to Peyronie's disease.
Online Patient Education in Obstructive Sleep Apnea: ChatGPT versus Google Search.
Is YouTube a sufficient source of information on Sarcoidosis?
Assessment of the Quality, Reliability, and Popularity of Information About Retinoblastoma on YouTube.
How Useful Is TikTok for Patients Searching for Carpal Tunnel Syndrome-Related Treatment Exercises?
The predation relationship between online medical search and online medical consultation-empirical research based on Baidu platform data.
Information Support or Emotional Support? Social Support in Online Health Information Seeking among Chinese Older Adults.
KNOWNET: Guided Health Information Seeking from LLMs via Knowledge Graph Integration.
RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature.
FloraNER: A new dataset for species and morphological terms named entity recognition in French botanical text.
Evidence library of meta-analytical literature assessing the sustainability of agriculture - a dataset.

J Educ Health Promot. 2024 ;13 175

The medical librarian's roles in the patient education process: A scoping review protocol.

Faezeh Amini, Hasan Ashrafi-Rizi, Parvaneh Khorasani, Mohammadreza Hashemian.

  BACKGROUND: The presence of medical librarians in the patient education team can greatly facilitate the patient education process. Expanding the role of medical librarians in patient education and using them in this process requires understanding the roles and services they can provide. This scoping review aims to identify different traditional and modern services and roles that medical librarians provide specifically in the patient education process.MATERIALS AND METHODS: A scoping review protocol is reported, according to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols statement and guided by The Joanna Briggs Institute. PubMed, Scopus, Web of Science, and LISTA (Library, Information Science & Technology Abstracts) will be searched. A grey literature search and hand-searching of citations and reference lists of the included studies will also be undertaken. Studies with their full text are not available and are in languages other than English will be excluded. Two independent reviewers will screen titles/abstracts and full text of retrieved articles and eligibility disagreements within a pair will resolve by discussion or a third reviewer. Data charting will be done in accordance with the data extraction tool made in Excel. Findings will be presented as a narrative summary supported by tables and diagrams.
CONCLUSIONS: Identifying the different services that medical librarians provide in the patient education process leads medical librarians to inform about the different services they can provide in the patient education process and to expand their roles as well as policymakers and hospital managers to be aware of these roles and use medical librarians in the patient education process appropriately. It also helps the general public and patients to learn about the services that medical librarians can provide them in this process.

Keywords:  Health education; health information professionals; medical librarian; patient education; professional role; scoping review

DOI:  https://doi.org/10.4103/jehp.jehp_438_23
J Community Health. 2024 Sep 06.

Assessing the Readiness of Rural Public Librarians to Implement Public Health Programs.

Noah Lenstra, Heather Franklin, Nathan F Dieckmann, Elena Andreyeva, Jay Maddock, Rebecca A Seguin-Fowler, Jim Winkle, Cynthia K Perry.

  Although health promotion is not the primary function of public libraries, it is well documented that many libraries engage in health promotion activities, even when resources are constrained. Less understood is the readiness of the public library workforce, particularly in rural communities, to implement evidence-based health promotion programs. This study uses a modified version of the Competency Assessment for Tier 2 Public Health Professionals to assess the readiness of a small sample (n = 21) of Oregon rural library managers to implement evidence-based health initiatives. Results show that outside of communication skills, most rural library workers do not consider themselves to have proficiency in core health promotion competencies. Although some slight differences were found among librarians based on socio-demographic factors, those differences were not statistically significant. Implications include the need for strengthened support to build the capacity for rural public library workers who are interested in delivering evidence-based health promotion programs.

Keywords:  Community health workforce; Health promotion; Rural health

DOI:  https://doi.org/10.1007/s10900-024-01402-0
IEEE Trans Vis Comput Graph. 2024 Sep 11. PP

VADIS: A Visual Analytics Pipeline for Dynamic Document Representation and Information-Seeking.

Rui Qiu, Yamei Tu, Po-Yin Yen, Han-Wei Shen.

In the biomedical domain, visualizing the document embeddings of an extensive corpus has been widely used in informationseeking tasks. However, three key challenges with existing visualizations make it difficult for clinicians to find information efficiently. First, the document embeddings used in these visualizations are generated statically by pretrained language models, which cannot adapt to the user's evolving interest. Second, existing document visualization techniques cannot effectively display how the documents are relevant to users' interest, making it difficult for users to identify the most pertinent information. Third, existing embedding generation and visualization processes suffer from a lack of interpretability, making it difficult to understand, trust and use the result for decision-making. In this paper, we present a novel visual analytics pipeline for user-driven document representation and iterative information seeking (VADIS). VADIS introduces a prompt-based attention model (PAM) that generates dynamic document embedding and document relevance adjusted to the user's query. To effectively visualize these two pieces of information, we design a new document map that leverages a circular grid layout to display documents based on both their relevance to the query and the semantic similarity. Additionally, to improve the interpretability, we introduce a corpus-level attention visualization method to improve the user's understanding of the model focus and to enable the users to identify potential oversight. This visualization, in turn, empowers users to refine, update and introduce new queries, thereby facilitating a dynamic and iterative information-seeking experience. We evaluated VADIS quantitatively and qualitatively on a real-world dataset of biomedical research papers to demonstrate its effectiveness.

DOI: https://doi.org/10.1109/TVCG.2024.3456339
Campbell Syst Rev. 2024 Sep;20(3): e1433

Searching for studies: A guide to information retrieval for Campbell systematic reviews.

Heather MacDonald, Cozette Comer, Margaret Foster, Patrick R Labelle, Scott Marsalis, Kate Nyhan, Zahra Premji, Morwenna Rogers, Ryan Splenda, Claire Stansfield, Sarah Young.

  This guide outlines general issues in searching for studies; describes the main sources of potential studies; and discusses how to plan the search process, design, and carry out search strategies, manage references found during the search process and document and report the search process.

Keywords:  search; systematic reviews

DOI:  https://doi.org/10.1002/cl2.1433
IEEE Trans Vis Comput Graph. 2024 Sep 10. PP

PUREsuggest: Citation-based Literature Search and Visual Exploration with Keyword-controlled Rankings.

Fabian Beck.

Citations allow quickly identifying related research. If multiple publications are selected as seeds, specifc suggestions for related literature can be made based on the number of incoming and outgoing citation links to this selection. Interactively adding recommended publications to the selection refnes the next suggestion and incrementally builds a relevant collection of publications. Following this approach, the paper presents a search and foraging approach, PUREsuggest, which combines citation-based suggestions with augmented visualizations of the citation network. The focus and novelty of the approach is, frst, the transparency of how the rankings are explained visually and, second, that the process can be steered through user-defned keywords, which refect topics of interests. The system can be used to build new literature collections, to update and assess existing ones, as well as to use the collected literature for identifying relevant experts in the feld. We evaluated the recommendation approach through simulated sessions and performed a user study investigating search strategies and usage patterns supported by the interface.

DOI: https://doi.org/10.1109/TVCG.2024.3456199
Heliyon. 2024 Aug 30. 10(16): e36362

Air mycobiome in the National Library of Greece following relocation to novel premises.

Stavroula Iliopoulou, Maria Kourteli, Athanasios Damialis, Evangelia Kapsanaki-Gotsi, Ioanna Pyrri.

  The aim of this work was to study the diversity and spatiotemporal fluctuations of airborne fungi in the National Library of Greece after its relocation from the Vallianeio historic building in the center of Athens to entirely new premises at the Stavros Niarchos Foundation Cultural Center, and also to compare the fungal aerosol in between the two sites. The air mycobiota were studied by a volumetric culture-based method, during the year 2019 in order to assess their diversity and abundance and to compare with those previously reported in the historic building. Twenty-eight genera of filamentous fungi were recovered indoors and 17 outdoors, in addition to yeasts registered as a group. The number of fungal genera recovered was almost similar in both premises, whereas seventeen genera indoors were identical, dominated by Penicillium, Cladosporium and Aspergillus. The mean daily fungal concentration was found to be 66 CFU m-3 indoors and 927 CFU m-3 outdoors in the new location vs 293 and 428 CFU m- 3 indoors and 707 and 648 CFU m- 3 outdoors in the previous one. The mean daily concentration indoors was consistently and significantly lower (P < 0.05) in the new building than in the historic one, although it was higher outdoors. The indoor/outdoor ratio for the total fungi was 0.07 in the new vs 0.41 and 0.66 in the previous one and reveals a superior indoor air quality in the new site. Air temperature and occupancy had a statistically significant impact on the concentration of indoor fungi. The remarkably reduced concentration of the mycobiota in the new premises indicated a considerable decline in fungal burden, mainly due to technological excellency of the facility and continuous preventive measures to ensure an enhanced indoor air quality in the National Library of Greece. This case study provides a paradigm about upgrading of indoor air after re-establishment of a facility in another setting.

Keywords:  Airborne fungi; Cultural heritage; Facility relocation; Indoor air; Microbial ecology

DOI:  https://doi.org/10.1016/j.heliyon.2024.e36362
Access Microbiol. 2024 ;pii: 000846.v3. [Epub ahead of print]6(9):

How to set up and run a Bad Bugs Bookclub group.

Joanna Verran.

  The Bad Bugs Bookclub is a public engagement initiative that enables scientists (microbiologists) and non-scientists to discuss the role of infectious disease and microorganisms in novels of fiction. The bookclub began in 2009, but since 2020, the meetings have taken place online, enabling international membership and occasional author participation. The bookclub has been shown, through peer-reviewed publications, to have impact and value to its members. For each book (the number now exceeds 100), a reading guide (questions to provoke discussion) and a meeting report (narrative of the discussion) were produced. Previously hosted on a website, the reading guides from this rich archive and resource are now presented alongside this paper, which provides tips on how to run a similar reading group.

Keywords:  bookclub; education; microbial literacy; public engagement

DOI:  https://doi.org/10.1099/acmi.0.000846.v3
J Med Internet Res. 2024 Sep 12. 26 e48257

Quality Measurement of Consumer Health Questions: Content and Language Perspectives.

Ashwag Alasmari, Lina Zhou.

  BACKGROUND: Health information consumers increasingly rely on question-and-answer (Q&A) communities to address their health concerns. However, the quality of questions posted significantly impacts the likelihood and relevance of received answers.OBJECTIVE: This study aims to improve our understanding of the quality of health questions within web-based Q&A communities.
METHODS: We develop a novel framework for defining and measuring question quality within web-based health communities, incorporating content- and language-based variables. This framework leverages k-means clustering and establishes automated metrics to assess overall question quality. To validate our framework, we analyze questions related to kidney disease from expert-curated and community-based Q&A platforms. Expert evaluations confirm the validity of our quality construct, while regression analysis helps identify key variables.
RESULTS: High-quality questions were more likely to include demographic and medical information than lower-quality questions (P<.001). In contrast, asking questions at the various stages of disease development was less likely to reflect high-quality questions (P<.001). Low-quality questions were generally shorter with lengthier sentences than high-quality questions (P<.01).
CONCLUSIONS: Our findings empower consumers to formulate more effective health information questions, ultimately leading to better engagement and more valuable insights within web-based Q&A communities. Furthermore, our findings provide valuable insights for platform developers and moderators seeking to enhance the quality of user interactions and foster a more trustworthy and informative environment for health information exchange.

Keywords:  ; consumer; health information; health information consumers; health questions; information behavior; information needs; information sharing; quality; quality measurement; question quality

DOI:  https://doi.org/10.2196/48257
Disabil Rehabil. 2024 Sep 09. 1-7

Content and readability of patient educational materials about neuromodulation for childhood movement disorders.

Jamie M Barnacoat, Jennifer Lewis, Kirsty Stewart, Shekeeb S Mohammad, Simon Paget.

  PURPOSE: To assess content and readability of online patient educational materials (PEMs) for paediatric deep brain stimulation (DBS) and intrathecal baclofen (ITB).METHODS: A content analysis of PEMs identified from top children's hospitals, institutions affiliated with published neuromodulation research, and DBS and ITB device manufacturers was conducted. PEM content was analysed using a predetermined framework. Readability was assessed using the Simple Measure of Gobbledygook (SMOG).
RESULTS: Of 109 PEMs (72 DBS; 37 ITB) identified, most (77 (71%)) originated in the United States. More ITB PEMs (27 (73%)) contained specific paediatric information than DBS PEMs (16 (22%)). PEMS more frequently described benefits (DBS: 92%; ITB: 89%) than risks (DBS: 49%; ITB: 78%). Frequent content included pre- and post-operative care, procedural details, and device information. Less common content included long-term lifestyle considerations, alternatives, patient experiences, and financial details. Median readability of PEMs was 13.2 (interquartile range [IQR]: 11.4-14.45) for DBS and 11.8 (IQR: 11-12.9) for ITB.
CONCLUSIONS: Available ITB and DBS PEMs often miss important broader details of the treatments, and have additional shortcomings such as poor readability scores. Our findings highlight need for more holistic content within neuromodulation PEMs, improved accessibility, and more balanced representation of risks and benefits.

Keywords:  Neuromodulation; deep brain stimulation; intrathecal baclofen; movement disorders; patient education

DOI:  https://doi.org/10.1080/09638288.2024.2397078
Facial Plast Surg. 2024 Sep 11.

Assessing the quality and readability of online patient information: ENT UK patient information e-leaflets vs responses by a Generative Artificial Intelligence.

Eamon Shamil, Tsz Ki Ko, Ka Siu Fan, James Schuster-Bruce, Mustafa Jaafar, Sadie Khwaja, Nicholas Eynon-Lewis, Alwyn Ray D'Souza, Peter Andrews.

BACKGROUND: The evolution of artificial intelligence has introduced new ways to disseminate health information, including natural language processing models like ChatGPT. However, the quality and readability of such digitally-generated information remains understudied. This study is the first to compare the quality and readability of digitally-generated health information against leaflets produced by professionals.METHODOLOGY: Patient information leaflets for five ENT UK leaflets and their corresponding ChatGPT responses were extracted from the Internet. Assessors with various degree of medical knowledge evaluated the content using the Ensuring Quality Information for Patients (EQIP) tool and readability tools including the Flesch-Kincaid Grade Level (FKGL). Statistical analysis was performed to identify differences between leaflets, assessors, and sources of information.
RESULTS: ENT UK leaflets were of moderate quality, scoring a median EQIP of 23. Statistically significant differences in overall EQIP score were identified between ENT UK leaflets but ChatGPT responses were of uniform quality. Non-specialist doctors rated the highest EQIP scores while medical students scored the lowest. The mean readability of ENT UK leaflets was higher than ChatGPT responses. The information metrics of ENT UK leaflets were moderate and varied between topics. Equivalent ChatGPT information provided comparable content quality, but with reduced readability.
CONCLUSIONS: ChatGPT patient information and professionally-produced leaflets had comparable content, but LLM content were required a higher reading age. With the increasing use of online health resources, this study highlights the need for a balanced approach that considers optimises both the quality and readability of patient education materials.

DOI: https://doi.org/10.1055/a-2413-3675
Medicine (Baltimore). 2024 May 31. 103(22): e38352

Assessing the readability, reliability, and quality of artificial intelligence chatbot responses to the 100 most searched queries about cardiopulmonary resuscitation: An observational study.

Dilek Ömür Arça, İsmail Erdemir, Fevzi Kara, Nurgazy Shermatov, Mürüvvet Odacioğlu, Emel İbişoğlu, Ferid Baran Hanci, Gönül Sağiroğlu, Volkan Hanci.

This study aimed to evaluate the readability, reliability, and quality of responses by 4 selected artificial intelligence (AI)-based large language model (LLM) chatbots to questions related to cardiopulmonary resuscitation (CPR). This was a cross-sectional study. Responses to the 100 most frequently asked questions about CPR by 4 selected chatbots (ChatGPT-3.5 [Open AI], Google Bard [Google AI], Google Gemini [Google AI], and Perplexity [Perplexity AI]) were analyzed for readability, reliability, and quality. The chatbots were asked the following question: "What are the 100 most frequently asked questions about cardio pulmonary resuscitation?" in English. Each of the 100 queries derived from the responses was individually posed to the 4 chatbots. The 400 responses or patient education materials (PEM) from the chatbots were assessed for quality and reliability using the modified DISCERN Questionnaire, Journal of the American Medical Association and Global Quality Score. Readability assessment utilized 2 different calculators, which computed readability scores independently using metrics such as Flesch Reading Ease Score, Flesch-Kincaid Grade Level, Simple Measure of Gobbledygook, Gunning Fog Readability and Automated Readability Index. Analyzed 100 responses from each of the 4 chatbots. When the readability values of the median results obtained from Calculators 1 and 2 were compared with the 6th-grade reading level, there was a highly significant difference between the groups (P < .001). Compared to all formulas, the readability level of the responses was above 6th grade. It can be seen that the order of readability from easy to difficult is Bard, Perplexity, Gemini, and ChatGPT-3.5. The readability of the text content provided by all 4 chatbots was found to be above the 6th-grade level. We believe that enhancing the quality, reliability, and readability of PEMs will lead to easier understanding by readers and more accurate performance of CPR. So, patients who receive bystander CPR may experience an increased likelihood of survival.

DOI: https://doi.org/10.1097/MD.0000000000038352
Laryngoscope Investig Otolaryngol. 2024 Oct;9(5): e70009

Search for medical information for chronic rhinosinusitis through an artificial intelligence ChatBot.

Arsany Yassa, Olivia Ayad, David Avery Cohen, Aman M Patel, Ved A Vengsarkar, Michael S Hegazin, Andrey Filimonov, Wayne D Hsueh, Jean Anderson Eloy.

  Objectives: Artificial intelligence is evolving and significantly impacting health care, promising to transform access to medical information. With the rise of medical misinformation and frequent internet searches for health-related advice, there is a growing demand for reliable patient information. This study assesses the effectiveness of ChatGPT in providing information and treatment options for chronic rhinosinusitis (CRS).Methods: Six inputs were entered into ChatGPT regarding the definition, prevalence, causes, symptoms, treatment options, and postoperative complications of CRS. International Consensus Statement on Allergy and Rhinology guidelines for Rhinosinusitis was the gold standard for evaluating the answers. The inputs were categorized into three categories and Flesch-Kincaid readability, ANOVA and trend analysis tests were used to assess them.
Results: Although some discrepancies were found regarding CRS, ChatGPT's answers were largely in line with existing literature. Mean Flesch Reading Ease, Flesch-Kincaid Grade Level and passive voice percentage were (40.7%, 12.15%, 22.5%) for basic information and prevalence category, (47.5%, 11.2%, 11.1%) for causes and symptoms category, (33.05%, 13.05%, 22.25%) for treatment and complications, and (40.42%, 12.13%, 18.62%) across all categories. ANOVA indicated no statistically significant differences in readability across the categories (p-values: Flesch Reading Ease = 0.385, Flesch-Kincaid Grade Level = 0.555, Passive Sentences = 0.601). Trend analysis revealed readability varied slightly, with a general increase in complexity.
Conclusion: ChatGPT is a developing tool potentially useful for patients and medical professionals to access medical information. However, caution is advised as its answers may not be fully accurate compared to clinical guidelines or suitable for patients with varying educational backgrounds.Level of evidence: 4.

Keywords:  ICAR; artificial intelligence; chronic rhinosinusitis; medical information systems; readability

DOI:  https://doi.org/10.1002/lio2.70009
Knee. 2024 Sep 05. pii: S0968-0160(24)00148-0. [Epub ahead of print]51 84-92

Accuracy assessment of ChatGPT responses to frequently asked questions regarding anterior cruciate ligament surgery.

Juan Bernardo Villarreal-Espinosa, Rodrigo Saad Berreta, Felicitas Allende, José Rafael Garcia, Salvador Ayala, Filippo Familiari, Jorge Chahla.

  BACKGROUND: The emergence of artificial intelligence (AI) has allowed users to have access to large sources of information in a chat-like manner. Thereby, we sought to evaluate ChatGPT-4 response's accuracy to the 10 patient most frequently asked questions (FAQs) regarding anterior cruciate ligament (ACL) surgery.METHODS: A list of the top 10 FAQs pertaining to ACL surgery was created after conducting a search through all Sports Medicine Fellowship Institutions listed on the Arthroscopy Association of North America (AANA) and American Orthopaedic Society of Sports Medicine (AOSSM) websites. A Likert scale was used to grade response accuracy by two sports medicine fellowship-trained surgeons. Cohen's kappa was used to assess inter-rater agreement. Reproducibility of the responses over time was also assessed.
RESULTS: Five of the 10 responses received a 'completely accurate' grade by two-fellowship trained surgeons with three additional replies receiving a 'completely accurate' status by at least one. Moreover, inter-rater reliability accuracy assessment revealed a moderate agreement between fellowship-trained attending physicians (weighted kappa = 0.57, 95% confidence interval 0.15-0.99). Additionally, 80% of the responses were reproducible over time.
CONCLUSION: ChatGPT can be considered an accurate additional tool to answer general patient questions regarding ACL surgery. None the less, patient-surgeon interaction should not be deferred and must continue to be the driving force for information retrieval. Thus, the general recommendation is to address any questions in the presence of a qualified specialist.

Keywords:  ACL surgery; AI; ChatGPT; Frequently asked questions (FAQs); Patient education

DOI:  https://doi.org/10.1016/j.knee.2024.08.014
Sex Med. 2024 Aug;12(4): qfae055

Prompt matters: evaluation of large language model chatbot responses related to Peyronie's disease.

Christopher J Warren, Victoria S Edmonds, Nicolette G Payne, Sandeep Voletti, Sarah Y Wu, JennaKay Colquitt, Hossein Sadeghi-Nejad, Nahid Punjani.

  Introduction: Despite direct access to clinicians through the electronic health record, patients are increasingly turning to the internet for information related to their health, especially with sensitive urologic conditions such as Peyronie's disease (PD). Large language model (LLM) chatbots are a form of artificial intelligence that rely on user prompts to mimic conversation, and they have shown remarkable capabilities. The conversational nature of these chatbots has the potential to answer patient questions related to PD; however, the accuracy, comprehensiveness, and readability of these LLMs related to PD remain unknown.Aims: To assess the quality and readability of information generated from 4 LLMs with searches related to PD; to see if users could improve responses; and to assess the accuracy, completeness, and readability of responses to artificial preoperative patient questions sent through the electronic health record prior to undergoing PD surgery.
Methods: The National Institutes of Health's frequently asked questions related to PD were entered into 4 LLMs, unprompted and prompted. The responses were evaluated for overall quality by the previously validated DISCERN questionnaire. Accuracy and completeness of LLM responses to 11 presurgical patient messages were evaluated with previously accepted Likert scales. All evaluations were performed by 3 independent reviewers in October 2023, and all reviews were repeated in April 2024. Descriptive statistics and analysis were performed.
Results: Without prompting, the quality of information was moderate across all LLMs but improved to high quality with prompting. LLMs were accurate and complete, with an average score of 5.5 of 6.0 (SD, 0.8) and 2.8 of 3.0 (SD, 0.4), respectively. The average Flesch-Kincaid reading level was grade 12.9 (SD, 2.1). Chatbots were unable to communicate at a grade 8 reading level when prompted, and their citations were appropriate only 42.5% of the time.
Conclusion: LLMs may become a valuable tool for patient education for PD, but they currently rely on clinical context and appropriate prompting by humans to be useful. Unfortunately, their prerequisite reading level remains higher than that of the average patient, and their citations cannot be trusted. However, given their increasing uptake and accessibility, patients and physicians should be educated on how to interact with these LLMs to elicit the most appropriate responses. In the future, LLMs may reduce burnout by helping physicians respond to patient messages.

Keywords:  Peyronie’s disease; artificial intelligence; chatbot; large language model; patient education

DOI:  https://doi.org/10.1093/sexmed/qfae055
Healthcare (Basel). 2024 Sep 05. pii: 1781. [Epub ahead of print]12(17):

Online Patient Education in Obstructive Sleep Apnea: ChatGPT versus Google Search.

Serena Incerti Parenti, Maria Lavinia Bartolucci, Elena Biondi, Alessandro Maglioni, Giulia Corazza, Antonio Gracco, Giulio Alessandri-Bonetti.

  The widespread implementation of artificial intelligence technologies provides an appealing alternative to traditional search engines for online patient healthcare education. This study assessed ChatGPT-3.5's capabilities as a source of obstructive sleep apnea (OSA) information, using Google Search as a comparison. Ten frequently searched questions related to OSA were entered into Google Search and ChatGPT-3.5. The responses were assessed by two independent researchers using the Global Quality Score (GQS), Patient Education Materials Assessment Tool (PEMAT), DISCERN instrument, CLEAR tool, and readability scores (Flesch Reading Ease and Flesch-Kincaid Grade Level). ChatGPT-3.5 significantly outperformed Google Search in terms of GQS (5.00 vs. 2.50, p < 0.0001), DISCERN reliability (35.00 vs. 29.50, p = 0.001), and quality (11.50 vs. 7.00, p = 0.02). The CLEAR tool scores indicated that ChatGPT-3.5 provided excellent content (25.00 vs. 15.50, p < 0.001). PEMAT scores showed higher understandability (60-91% vs. 44-80%) and actionability for ChatGPT-3.5 (0-40% vs. 0%). Readability analysis revealed that Google Search responses were easier to read (FRE: 56.05 vs. 22.00; FKGL: 9.00 vs. 14.00, p < 0.0001). ChatGPT-3.5 delivers higher quality and more comprehensive OSA information compared to Google Search, although its responses are less readable. This suggests that while ChatGPT-3.5 can be a valuable tool for patient education, efforts to improve readability are necessary to ensure accessibility and utility for all patients. Healthcare providers should be aware of the strengths and weaknesses of various healthcare information resources and emphasize the importance of critically evaluating online health information, advising patients on its reliability and relevance.

Keywords:  artificial intelligence; obstructive sleep apnea; patient education; search engine

DOI:  https://doi.org/10.3390/healthcare12171781
Respir Res. 2024 Sep 09. 25(1): 334

Is YouTube a sufficient source of information on Sarcoidosis?

Katharina Buschulte, Sarah El-Hadi, Philipp Höger, Claudia Ganter, Marlies Wijsenbeek, Nicolas Kahn, Katharina Kriegsmann, Gillian C Goobie, Christopher J Ryerson, Markus Polke, Franziska Trudzinski, Michael Kreuter.

  BACKGROUND: The internet is a common source of health information for patients and caregivers. To date, content and information quality of YouTube videos on sarcoidosis has not been studied. The aim of our study was to investigate the content and quality of information on sarcoidosis provided by YouTube videos.METHODS: Of the first 200 results under the search term "sarcoidosis," all English-language videos with content directed at patients were included. Two independent investigators assessed the content of the videos based on 25 predefined key features (content score with 0-25 points), as well as reliability and quality (HONCode score with 0-8 points, DISCERN score with 1-5 points). Misinformation contained in the videos was described qualitatively.
RESULTS: The majority of the 85 included videos were from an academic or governmental source (n = 63, 74%), and median time since upload was 33 months (IQR 10-55). Median video duration was 8 min (IQR 3-13) and had a median of 2,044 views (IQR 504 - 13,203). Quality assessment suggested partially sufficient information: mean HONCode score was 4.4 (SD 0.9) with 91% of videos having a medium quality HONCode evaluation. Mean DISCERN score was 2.3 (SD 0.5). Video content was generally poor with a mean of 10.5 points (SD 0.6). Frequently absent key features included information on the course of disease (6%), presence of substantial geographical variation (7%), and importance of screening for extrapulmonary manifestations (11%). HONCode scores were higher in videos from academic or governmental sources (p = 0.003), particularly regarding "transparency of sponsorship" (p < 0.001). DISCERN and content scores did not differ by video category.
CONCLUSIONS: Most YouTube videos present incomplete information reflected in a poor content score, especially regarding screening for extrapulmonary manifestations. Quality was partially sufficient with higher scores in videos from academic or governmental sources, but often missing references and citing specific evidence. Improving patient access to trustworthy and up to date information is needed.

Keywords:  Content; Information; Quality; Sarcoidosis; Videos; YouTube

DOI:  https://doi.org/10.1186/s12931-024-02956-2
J Pediatr Ophthalmol Strabismus. 2024 Sep 10. 1-6

Assessment of the Quality, Reliability, and Popularity of Information About Retinoblastoma on YouTube.

Berkay Öner Karaca, Adem Tellioğlu.

PURPOSE: To evaluate the quality, reliability, and popularity of YouTube videos addressing retinoblastoma.METHODS: This was a retrospective, cross-sectional, register-based study. A YouTube search was performed using the keyword retinoblastoma and the first 100 videos that came out were included in the study. Duplicate videos, videos that were not in English, and videos that were less than 1 minute were excluded. The number of views, likes, dislikes, comments, video type (uploaded by physicians, uploaded by public or private institution, uploaded by health channel or uploaded by patients), and country of origin were evaluated for all videos. The popularity of the videos was evaluated with the Video Power Index. The quality of the videos was measured using the DISCERN score (DISCERN), Journal of the American Medical Association (JAMA) benchmark criteria, and Global Quality Score (GQS).
RESULTS: Of the 100 videos, 70 videos met the criteria and were included in the study. The mean DISCERN, JAMA, and GQS scores were 42.54 ± 18.77, 2.14 ± 1.03, 2.87 ± 1.42 and 2.99 ± 1.44, respectively. On examining the upload source 18 (25.7%) videos were uploaded by private institutions, 15 (21.4%) videos by physicians, 14 (20.0%) videos by public institutions, 14 (20.0%) videos by health channels, and 9 (12.9%) videos by patients. There was a significant level of agreement between the two commentators evaluating the videos with a power of 91.6% (kappa score: 0.916). Videos uploaded by physicians and public or private institutions had significantly higher DISCERN, JAMA, and GQS scores.
CONCLUSIONS: The content of YouTube videos regarding retinoblastoma is generally of moderate quality for patients. Increasing the number of videos uploaded by physicians and public or private institutions will increase the quality, reliability, and informative value of the videos. [J Pediatr Ophthalmol Strabismus. 20XX;X(X):XXX-XXX.].

DOI: https://doi.org/10.3928/01913913-20240807-03
Healthcare (Basel). 2024 Aug 26. pii: 1697. [Epub ahead of print]12(17):

How Useful Is TikTok for Patients Searching for Carpal Tunnel Syndrome-Related Treatment Exercises?

Damon V Briggs, Albert T Anastasio, Mikhail A Bethell, Joshua R Taylor, Marc J Richard, Christopher S Klifto.

  Since orthopedic surgery has been slower to acknowledge the rise of social media for distributing medical information, this study aims to evaluate TikTok videos' quality and educational value in relation to carpal tunnel syndrome treatment exercises. TikTok was searched using the hashtags "#carpaltunnelexercises", "#carpaltunnelremedies", "#carpaltunnelrehab", and "#physicaltherapyforcarpaltunnel". The engagement indicators were documented and the video content quality was assessed using the DISCERN, CTEES, JAMA, and GQS grading scales. There were 101 videos included, which accumulated 20,985,730 views. The videos received 1,460,953 likes, 15,723 comments, 243,245 favorites, and 159,923 shares. Healthcare professionals were responsible for 72% of the video uploads, whereas general users contributed 28%. More healthcare professionals' videos were graded as "poor" (79%) compared to general users (21%). General users received slightly more video grades of "very poor" (52%) than healthcare professionals (48%). For the DISCERN grading, the videos by healthcare professionals were significantly better than those by general users in terms of reliability, achieving aims, and relevancy. They were also superior in the overall composition of the health information derived from the total DISCERN score. However, no significant differences were found between the two groups when using the CTEES, JAMA, and GQS grading scales. Overall, despite the emergence of TikTok as a medical information tool, the quality and educational value of the carpal tunnel syndrome exercise videos were poor.

Keywords:  DISCERN; TikTok; carpal tunnel; educational value; social media

DOI:  https://doi.org/10.3390/healthcare12171697
Front Public Health. 2024 ;12 1392743

The predation relationship between online medical search and online medical consultation-empirical research based on Baidu platform data.

Yang Wang, Lingshi Ran, Wei Jiao, Yixue Xia, Yuexin Lan.

  Introduction: This study investigates the mutual influence between online medical search and online medical consultation. It focuses on understanding the health information needs that drive these health information-seeking behaviors by utilizing insights from behavioral big data.Methods: We used actual behavioral data from Chinese internet users on Baidu platform's "Epidemic Index" from November 26, 2022, to January 25, 2023. Data modeling was conducted to ensure the reliability of the model. Drawing on the logistic model, we constructed a foundational model to quantify the evolutionary patterns of online medical search and online medical consultation. An impact function was defined to measure their mutual influence. Additionally, a pattern detection experiment was conducted to determine the structure of the impact function with maximum commonality through data fitting.
Results: The analysis allowed us to build a mathematical model that quantifies the nonlinear correlation between online medical search and online medical consultation. Numerical analysis revealed a predation mechanism between online medical consultation and online medical search, highlighting the role of health information needs in this dynamic.
Discussion: This study offers a novel practical approach to better meet the public's health information needs by understanding the interplay between online medical search and consultation. Additionally, the modeling method used here is broadly applicable, providing a framework for quantifying nonlinear correlations among different behaviors when appropriate data is available.

Keywords:  evolutionary patterns; health information needs; health information-seeking behavior; online medical consultation; online medical search

DOI:  https://doi.org/10.3389/fpubh.2024.1392743
Healthcare (Basel). 2024 Sep 07. pii: 1790. [Epub ahead of print]12(17):

Information Support or Emotional Support? Social Support in Online Health Information Seeking among Chinese Older Adults.

Xiaowen Zhu, Chang Li.

  Online Health Information Seeking (OHIS) serves as an alternative form of social capital that can help older adults alleviate offline medical-related stress. This study collected and analyzed user interaction data from Patient-to-Doctor and Patient-to-Peer platforms and compared the roles of social support between them. Significant differences were identified in the dimensions of social support (information, emotional, and companion) on the Patient-to-Peer platforms compared with Patient-to-Doctor platforms (p < 0.05). The overall and core-core network density values for social support on Patient-to-Peer platforms were higher than those on Patient-to-Doctor platforms. Patient-to-Doctor interactions focused on information support, displaying a more centralized and efficient network with structural holes pertaining to treatment effects. By contrast, Patient-to-Peer interactions provided more emotional support, with a dispersed and redundant network containing structural holes related to individual information. Companion support was found to be weaker on both platforms. Additionally, digital literacy, surrogate seeking, and altruistic information significantly explained the variances between the two platforms (p < 0.01), with surrogate seeking playing a crucial role. These findings enhance our understanding of OHIS disparities among older adults and their surrogates, offering valuable insights for developing effective support systems and regulatory frameworks for health information platforms.

Keywords:  older adult healthcare; online health information seeking; online platforms; social network analysis; social support

DOI:  https://doi.org/10.3390/healthcare12171790
IEEE Trans Vis Comput Graph. 2024 Sep 10. PP

KNOWNET: Guided Health Information Seeking from LLMs via Knowledge Graph Integration.

Youfu Yan, Yu Hou, Yongkang Xiao, Rui Zhang, Qianwen Wang.

The increasing reliance on Large Language Models (LLMs) for health information seeking can pose severe risks due to the potential for misinformation and the complexity of these topics. This paper introduces KNOWNET a visualization system that integrates LLMs with Knowledge Graphs (KG) to provide enhanced accuracy and structured exploration. Specifically, for enhanced accuracy, KNOWNET extracts triples (e.g., entities and their relations) from LLM outputs and maps them into the validated information and supported evidence in external KGs. For structured exploration, KNOWNET provides next-step recommendations based on the neighborhood of the currently explored entities in KGs, aiming to guide a comprehensive understanding without overlooking critical aspects. To enable reasoning with both the structured data in KGs and the unstructured outputs from LLMs, KNOWNET conceptualizes the understanding of a subject as the gradual construction of graph visualization. A progressive graph visualization is introduced to monitor past inquiries, and bridge the current query with the exploration history and next-step recommendations. We demonstrate the effectiveness of our system via use cases and expert interviews.

DOI: https://doi.org/10.1109/TVCG.2024.3456364
Database (Oxford). 2024 Sep 12. pii: baae095. [Epub ahead of print]2024

RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature.

Katerina Nastou, Farrokh Mehryary, Tomoko Ohta, Jouni Luoma, Sampo Pyysalo, Lars Juhl Jensen.

In the field of biomedical text mining, the ability to extract relations from the literature is crucial for advancing both theoretical research and practical applications. There is a notable shortage of corpora designed to enhance the extraction of multiple types of relations, particularly focusing on proteins and protein-containing entities such as complexes and families, as well as chemicals. In this work, we present RegulaTome, a corpus that overcomes the limitations of several existing biomedical relation extraction (RE) corpora, many of which concentrate on single-type relations at the sentence level. RegulaTome stands out by offering 16 961 relations annotated in >2500 documents, making it the most extensive dataset of its kind to date. This corpus is specifically designed to cover a broader spectrum of >40 relation types beyond those traditionally explored, setting a new benchmark in the complexity and depth of biomedical RE tasks. Our corpus both broadens the scope of detected relations and allows for achieving noteworthy accuracy in RE. A transformer-based model trained on this corpus has demonstrated a promising F1-score (66.6%) for a task of this complexity, underscoring the effectiveness of our approach in accurately identifying and categorizing a wide array of biological relations. This achievement highlights RegulaTome's potential to significantly contribute to the development of more sophisticated, efficient, and accurate RE systems to tackle biomedical tasks. Finally, a run of the trained RE system on all PubMed abstracts and PMC Open Access full-text documents resulted in >18 million relations, extracted from the entire biomedical literature.

DOI: https://doi.org/10.1093/database/baae095
Data Brief. 2024 Oct;56 110824

FloraNER: A new dataset for species and morphological terms named entity recognition in French botanical text.

Ayoub Nainia, Régine Vignes-Lebbe, Eric Chenin, Maya Sahraoui, Hajar Mousannif, Jihad Zahir.

  FloraNER is a distantly supervised named entity recognition dataset (NER). The dataset is built from botanical French literature extracted from the OCR-preprocessed flora of New Caledonia, provided by the National Museum of Natural History in France (MNHN), and distantly annotated with a botanical French corpus created by merging botanical lexicons available online. FloraNER comprises separate sub-datasets for the recognition of plant species names, as well as coarse-grained and fine-grained botanical morphological terms. The resulting datasets are in CSV format, displaying textual data, identified named entities, and their annotations, covering one named entity type "Species" (Espèce in French) for species name identification, two named entity types "Organ" and "Descriptor" for coarse-grained morphological term identification, and eight named entity types for fine-grained morphological term identification: Organ, Descriptor, Form, Color, Development, Structure, Surface, Position, Disposition, and Measure. This dataset can be utilized to train and evaluate named entity recognition models for extracting information from botanical French literature.

Keywords:  Biodiversity dataset; NER Dataset; Plant morphology dataset; Species identification dataset

DOI:  https://doi.org/10.1016/j.dib.2024.110824
Sci Data. 2024 Sep 07. 11(1): 979

Evidence library of meta-analytical literature assessing the sustainability of agriculture - a dataset.

Andrea Schievano, Marta Pérez-Soba, Simona Bosco, Ana Montero-Castaño, Rui Catarino, Mathilde Chen, Giovanni Tamburini, Beatrice Landoni, Otho Mantegazza, Irene Guerrero, Maria Bielza, Michael Assouline, Renate Koeble, Frank Dentener, Marijn Van der Velde, Carlo Rega, Andrea Furlan, Maria Luisa Paracchini, Franz Weiss, Vincenzo Angileri, Jean-Michel Terres, David Makowski.

In the last two decades, an exponentially growing number of meta-analyses (MAs) synthesize thousands of peer-reviewed studies on the environmental impacts of farming practices (FPs). This paper describes the iMAP-FP evidence library, a comprehensive dataset on the effects of 34 categories of FPs (such as agronomic practices, cropping and livestock systems, land management options and mitigation techniques) on 34 impacts including climate mitigation, soil health, environmental pollution, water use, nutrients cycling, biodiversity, and agricultural productivity. Through systematic screening, 570 MAs published since 2000 were selected and categorized according to the type of FP. We assessed their impacts, the geographic regions covered, and their quality. We extracted 3,811 effects and their statistical significance associated with sustainable FPs (intervention) compared to a control (typically conventional agriculture) across 223 different intervention-control pairs. Our dataset is accompanied with an online free-access library, which includes a catalogue of synthetic reports summarizing the available evidence on each evaluated FP.

DOI: https://doi.org/10.1038/s41597-024-03682-6