Objective: To evaluate the quality of information available on the Internet to patients with a cervical pathology undergoing elective cervical spine surgery.
Methods: Six key words ("cervical discectomy," "cervical foraminotomy," "cervical fusion," "cervical disc replacement," "cervical arthroplasty," "cervical artificial disc") were entered into two different search engines (Google, Yahoo!). For each key word, the first 50 websites were evaluated for accessibility, comprehensibility, and website quality using the DISCERN tool, transparency and honesty criteria, and an accuracy and exhaustivity scale.
Results: Of 5,098,500 evaluable websites, 600 were visited; 97 (16%) of these websites were evaluated for quality and comprehensiveness. Overall, 3% of sites obtained an excellent global quality score, 7% obtained a good score, 25% obtained an above average score, 15% obtained an average score, 37% obtained a poor score, and 13% obtained a very poor score. High-quality websites were affiliated with a professional society (P = 0.021), had bibliographical references (P = 0.030), and had a recent update within 6 months (r = 0.277, P < 0.001). No correlation between global quality score and other variables was observed.
Conclusions: This study shows that the search for medical information on the Internet is time-consuming and often disappointing. The Internet is a potentially misleading source of information. Surgeons and professional societies must use the Internet as an ally in providing optimal information to patients.
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http://dx.doi.org/10.1016/j.wneu.2012.11.003 | DOI Listing |
J Med Internet Res
December 2024
University Clinic for Interdisciplinary Orthopedic Pathways (UCOP), Elective Surgery Center, Silkeborg Regional Hospital, Silkeborg, Denmark.
Background: Access to clear and comprehensible health information is crucial for patient empowerment, leading to improved self-care, adherence to treatment plans, and overall health outcomes. Traditional methods of information delivery, such as written documents and oral communication, often result in poor memorization and comprehension. Recent innovations, such as animation videos, have shown promise in enhancing patient understanding, but comprehensive investigations into their effectiveness across various health care settings are lacking.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
Guangzhou Cadre and Talent Health Management Center, Guangzhou, China.
Background: Large language models have shown remarkable efficacy in various medical research and clinical applications. However, their skills in medical image recognition and subsequent report generation or question answering (QA) remain limited.
Objective: We aim to finetune a multimodal, transformer-based model for generating medical reports from slit lamp images and develop a QA system using Llama2.
J Med Internet Res
December 2024
Institute for Musculoskeletal Health, Sydney Local Health District, Sydney, Australia.
Background: Advanced technologies are becoming increasingly accessible in rehabilitation. Current research suggests technology can increase therapy dosage, provide multisensory feedback, and reduce manual handling for clinicians. While more high-quality evidence regarding the effectiveness of rehabilitation technologies is needed, understanding of how to effectively integrate technology into clinical practice is also limited.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Cancer Rehabilitation and Survivorship, Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Background: Virtual follow-up (VFU) has the potential to enhance cancer survivorship care. However, a greater understanding is needed of how VFU can be optimized.
Objective: This study aims to examine how, for whom, and in what contexts VFU works for cancer survivorship care.
PLoS One
January 2025
School of Economics & Management, Beijing Information Science & Technology University, Beijing, China.
E-commerce faces challenges such as content homogenization and high perceived risk among users. This paper aims to predict perceived risk in different contexts by analyzing review content and website information. Based on a dataset containing 262,752 online reviews, we employ the KeyBERT-TextCNN model to extract thematic features from the review content.
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