bims-librar Biomed News
on Biomedical librarianship
Issue of 2023‒12‒10
seven papers selected by
Thomas Krichel, Open Library Society
  1. Systematic review search strategies are poorly reported and not reproducible: a cross-sectional meta-research study.
  2. Collaborating with AI in literature search-An important frontier.
  3. AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model.
  4. Quality and Readability Analysis of Online Information on First Metatarsophalangeal Joint Fusion.
  5. YouTube® as a source of information for Spanish-speaking patients with rheumatoid arthritis.
  6. Exploring YouTube Videos About Anorexia Nervosa on the Basis of Reliability, Popularity, and Contributions of Healthcare Professionals: A Cross-Sectional Study.
  7. Automated classification of lay health articles using natural language processing: a case study on pregnancy health and postpartum depression.
  1. J Clin Epidemiol. 2023 Dec 03. pii: S0895-4356(23)00319-0. [Epub ahead of print] 111229
    Systematic review search strategies are poorly reported and not reproducible: a cross-sectional meta-research study.
    Melissa L Rethlefsen, Tara J Brigham, Carrie Price, David Moher, Lex M Bouter, Jamie J Kirkham, Sara Schroter, Maurice P Zeegers.
      OBJECTIVE: To determine the reproducibility of biomedical systematic review search strategies.STUDY DESIGN AND SETTING: A cross-sectional reproducibility study was conducted on a random sample of 100 systematic reviews indexed in MEDLINE in November 2021. The primary outcome measure is the percentage of systematic reviews for which all database searches can be reproduced, operationalized as fulfilling 6 key PRISMA-S reporting guideline items and having all database searches reproduced within 10% of the number of original results. Key reporting guideline items included database name, multi-database searching, full search strategies, limits and restrictions, date(s) of searches, and total records.
    RESULTS: The 100 systematic review articles contained 453 database searches. Only 22 (4.9%) database searches reported all six PRISMA-S items. Forty-seven (10.4%) database searches could be reproduced within 10% of the number of results from the original search; 6 searches differed by more than 1000% between the originally reported number of results and the reproduction. Only one systematic review article provided the necessary search details to be fully reproducible.
    CONCLUSION: Systematic review search reporting is poor. To correct this will require a multi-faceted response from authors, peer reviewers, journal editors, and database providers.
    Keywords:  database searches; reporting guidelines; reproducibility; search strategies; systematic reviews; transparency
    DOI:  https://doi.org/10.1016/j.jclinepi.2023.111229
  2. Hepatol Commun. 2023 Dec 01. pii: e0336. [Epub ahead of print]7(12):
    Collaborating with AI in literature search-An important frontier.
    Masaru Enomoto, Cheng-Hao Tseng, Yao-Chun Hsu, Le Thi Thanh Thuy, Mindie H Nguyen.
      
    DOI:  https://doi.org/10.1097/HC9.0000000000000336
  3. Cureus. 2023 Nov;15(11): e49764
    AI-Generated Information for Vascular Patients: Assessing the Standard of Procedure-Specific Information Provided by the ChatGPT AI-Language Model.
    Omar Haidar, Alexander Jaques, Pierre W McCaughran, Matthew J Metcalfe.
      Introduction Ensuring access to high-quality information is paramount to facilitating informed surgical decision-making. The use of the internet to access health-related information is increasing, along with the growing prevalence of AI language models such as ChatGPT. We aim to assess the standard of AI-generated patient-facing information through a qualitative analysis of its readability and quality. Materials and methods We performed a retrospective qualitative analysis of information regarding three common vascular procedures: endovascular aortic repair (EVAR), endovenous laser ablation (EVLA), and femoro-popliteal bypass (FPBP). The ChatGPT responses were compared to patient information leaflets provided by the vascular charity, Circulation Foundation UK. Readability was assessed using four readability scores: the Flesch-Kincaid reading ease (FKRE) score, the Flesch-Kincaid grade level (FKGL), the Gunning fog score (GFS), and the simple measure of gobbledygook (SMOG) index. Quality was assessed using the DISCERN tool by two independent assessors. Results The mean FKRE score was 33.3, compared to 59.1 for the information provided by the Circulation Foundation (SD=14.5, p=0.025) indicating poor readability of AI-generated information. The FFKGL indicated that the expected grade of students likely to read and understand ChatGPT responses was consistently higher than compared to information leaflets at 12.7 vs. 9.4 (SD=1.9, p=0.002). Two metrics measure readability in terms of the number of years of education required to understand a piece of writing: the GFS and SMOG. Both scores indicated that AI-generated answers were less accessible. The GFS for ChatGPT-provided information was 16.7 years versus 12.8 years for the leaflets (SD=2.2, p=0.002) and the SMOG index scores were 12.2 and 9.4 years for ChatGPT and the patient information leaflets, respectively (SD=1.7, p=0.001). The DISCERN scores were consistently higher in human-generated patient information leaflets compared to AI-generated information across all procedures; the mean score for the information provided by ChatGPT was 50.3 vs. 56.0 for the Circulation Foundation information leaflets (SD=3.38, p<0.001). Conclusion We concluded that AI-generated information about vascular surgical procedures is currently poor in both the readability of text and the quality of information. Patients should be directed to reputable, human-generated information sources from trusted professional bodies to supplement direct education from the clinician during the pre-procedure consultation process.
    Keywords:  ai; artificial intelligence; chatgpt; patient education; vascular
    DOI:  https://doi.org/10.7759/cureus.49764
  4. J Foot Ankle Surg. 2023 Dec 01. pii: S1067-2516(23)00308-3. [Epub ahead of print]
    Quality and Readability Analysis of Online Information on First Metatarsophalangeal Joint Fusion.
    Hamza M Raja, Sterling J DeShazo, Jeffrey T Bowcutt, Jie Chen.
      Patients frequently turn to the internet to learn about their orthopedic procedures. This study evaluates the readability and quality of first metatarsophalangeal (MTP) joint fusion information found online. We evaluated websites based on classification, search term, readability, HON code, DISCERN score, Journal of the American Medical Association benchmark criteria, and an author-created MTP fusion index (MFI). The average readability of websites was 8.48 ± 1.99, above the recommended sixth- or eighth-grade reading level. Almost half of all websites (48.98%) provided "poor" information. Keywords had no significant impact on the readability or quality of information. Academic/governmental websites had the highest quality of information, with the highest DISCERN and second highest MFI. Most websites (52.04%) were commercial and were the easiest to read, but had the second lowest DISCERN and MFI scores. Our results suggest that inappropriate information on the MTP joint fusion procedure is abundant online. Academic/governmental websites have the highest quality of information, but may be difficult for patients to comprehend. Many websites do have readable and relevant information. We recommend that physicians create a list of websites with accurate, relevant information for patients to circumvent the misinformation they may find while navigating and reading online.
    Keywords:  internet; joint fusion; metatarsophalangeal joint; online information; patient education
    DOI:  https://doi.org/10.1053/j.jfas.2023.11.013
  5. Reumatol Clin (Engl Ed). 2023 Dec;pii: S2173-5743(23)00111-9. [Epub ahead of print]19(10): 571-578
    YouTube® as a source of information for Spanish-speaking patients with rheumatoid arthritis.
    Julián E Barahona-Correa, Camilo Rueda-Ortiz, Oscar Muñoz, Ángel Alberto García, Daniel G Fernández-Ávila.
      INTRODUCTION: Social media (SoMe) has reshaped access to health information, which may benefit patients with rheumatoid arthritis (RA), although an evaluation of the characteristics of contents for Spanish-speaking patients is lacking. We aimed to assess patient engagement, reliability, comprehensiveness, and quality of data uploaded to YouTube® for Spanish-speaking patients.METHODS: We evaluated the videos uploaded to YouTube® in Spanish about RA. Information about video length, engagement (i.e., views, likes, popularity index), time online, and the source was retrieved; we appraised reliability (DISCERN), comprehensiveness (content score), and quality (Global Quality Score) using standardized scores.
    RESULTS: We included 200 videos in the study and classified 67% of the videos as useful. These videos had a higher number of views (19,491 [10,132-61,162] vs. 11,208 [8183-20,538]), a longer time online (1156 [719-2254] vs. 832 [487-1708] days), and a shorter duration (6.3 [3.4-15.8] vs. 11.8 [7.4-20.3] min). Engagement parameters were similar between useful and misleading videos. Useful videos had higher reliability, comprehensiveness, and quality scores. Useful videos were mainly uploaded by independent users and government/news agencies; academic organizations offered only 15% of useful videos.
    CONCLUSIONS: Most of the information in YouTube® for Spanish-speaking patients with RA is useful; however, patient engagement is similar between useful and misleading content. More substantial involvement of academia in developing high-quality educational multimedia is warranted.
    Keywords:  Artritis reumatoide; Educación; Educación del paciente; Education; Medios de comunicación sociales; Patient education; Rheumatoid arthritis; Social media
    DOI:  https://doi.org/10.1016/j.reumae.2023.05.006
  6. Cureus. 2023 Nov;15(11): e48095
    Exploring YouTube Videos About Anorexia Nervosa on the Basis of Reliability, Popularity, and Contributions of Healthcare Professionals: A Cross-Sectional Study.
    Ananthakrishnan Suresh, Lalitha Lalithya Pallempati, Palak Saxena, Ayesha Ansari, Radhika Bassi, Ajita Bhandari.
      BACKGROUND:  Anorexia nervosa, an eating disorder, is characterized by a distorted body image, intense fear of gaining weight, and self-imposed starvation. The aim of this study is to analyze type of information as well as its quality and reliability on YouTube about anorexia nervosa.  Methodology: A cross-sectional observational study was conducted on 59 Youtube videos using anorexia nervosa-related keywords in June 2023. The characteristics of the YouTube videos, such as the language of the information, the time of upload, and the qualifications of the uploaders, were recorded. The content and quality of 59 videos were assessed using the Global Quality Score (GQS) and reliability grading systems.RESULTS: The videos accumulated a total of 256,602 likes, 5,644 dislikes, and 17,761 comments. Treatment-related content accounted for 81.36% of the videos, while descriptions of symptoms comprised 79.66%. Doctors contributed to 18.6% of the total uploads, making them the second-largest group of uploaders after the 'Other' category. The median reliability score for doctors is 4, the same as the hospital healthcare organization. This indicates that the perceived reliability of doctors as a source of information is as high as that of hospital healthcare organizations.
    CONCLUSIONS: In conclusion, this study highlights the importance of critically evaluating information on anorexia nervosa videos on YouTube. Despite variations in popularity, the overall quality and reliability remained consistent. Doctors were perceived as reliable sources of information, comparable to hospital healthcare organizations. Ensuring accurate and trustworthy content is crucial for supporting those affected by anorexia nervosa and promoting reliable information to the public.
    Keywords:  anorexia nervosa; content analysis; eating disorder; healthcare professionals; perception of reliability; quality assessment; symptoms; treatment; youtube
    DOI:  https://doi.org/10.7759/cureus.48095
  7. Front Psychiatry. 2023 ;14 1258887
    Automated classification of lay health articles using natural language processing: a case study on pregnancy health and postpartum depression.
    Braja Gopal Patra, Zhaoyi Sun, Zilin Cheng, Praneet Kasi Reddy Jagadeesh Kumar, Abdullah Altammami, Yiyang Liu, Rochelle Joly, Caroline Jedlicka, Diana Delgado, Jyotishman Pathak, Yifan Peng, Yiye Zhang.
      Objective: Evidence suggests that high-quality health education and effective communication within the framework of social support hold significant potential in preventing postpartum depression. Yet, developing trustworthy and engaging health education and communication materials requires extensive expertise and substantial resources. In light of this, we propose an innovative approach that involves leveraging natural language processing (NLP) to classify publicly accessible lay articles based on their relevance and subject matter to pregnancy and mental health.Materials and methods: We manually reviewed online lay articles from credible and medically validated sources to create a gold standard corpus. This manual review process categorized the articles based on their pertinence to pregnancy and related subtopics. To streamline and expand the classification procedure for relevance and topics, we employed advanced NLP models such as Random Forest, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer model (gpt-3.5-turbo).
    Results: The gold standard corpus included 392 pregnancy-related articles. Our manual review process categorized the reading materials according to lifestyle factors associated with postpartum depression: diet, exercise, mental health, and health literacy. A BERT-based model performed best (F1 = 0.974) in an end-to-end classification of relevance and topics. In a two-step approach, given articles already classified as pregnancy-related, gpt-3.5-turbo performed best (F1 = 0.972) in classifying the above topics.
    Discussion: Utilizing NLP, we can guide patients to high-quality lay reading materials as cost-effective, readily available health education and communication sources. This approach allows us to scale the information delivery specifically to individuals, enhancing the relevance and impact of the materials provided.
    Keywords:  health communication; natural language processing; online health information; postpartum depression; pregnancy
    DOI:  https://doi.org/10.3389/fpsyt.2023.1258887
This page is being maintained by Thomas Krichel. It was last updated on 2023‒12‒10 at 19:01.