Purpose: To critically evaluate the quality, accuracy, and readability of readily available Internet patient resources for platelet-rich plasma (PRP) as a treatment modality for musculoskeletal injuries.
Methods: Using the 3 most commonly used Internet search engines (Google, Bing, Yahoo), the search term "platelet rich plasma" was entered, and the first 50 websites from each search were reviewed. The website's affiliation was identified. Quality was evaluated using 25-point criteria based on guidelines published by the American Academy of Orthopaedic Surgeons, and accuracy was assessed with a previously described 12-point grading system by 3 reviewers independently. Readability was evaluated using the Flesch-Kincaid (FK) grade score.
Results: A total of 46 unique websites were identified and evaluated. The average quality and accuracy was 9.4 ± 3.4 (maximum 25) and 7.9 ± 2.3 (maximum 12), respectively. The average FK grade level was 12.6 ± 2.4, which is several grades higher than the recommended eighth-grade level for patient education material. Ninety-one percent (42/46) of websites were authored by physicians, and 9% (4/46) contained commercial bias. Mean quality was significantly greater in websites authored by health care providers (9.8 ± 3.1 vs 5.9 ± 4.7, P = .029) and in websites without commercial bias (9.9 ± 3.1 vs 4.5 ± 3.2, P = .002). Mean accuracy was significantly lower in websites authored by health care providers (7.6 ± 2.2 vs 11.0 ± 1.2, P = .004). Only 24% (11/46) reported that PRP remains an investigational treatment.
Conclusions: The accuracy and quality of online patient resources for PRP are poor, and the information overestimates the reading ability of the general population. Websites authored by health care providers had higher quality but lower accuracy. Additionally, the majority of websites do not identify PRP as an experimental treatment, which may fail to provide appropriate patient understanding and expectations.
Clinical Relevance: Physicians should educate patients that many online patient resources have poor quality and accuracy and can be difficult to read.
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http://dx.doi.org/10.1016/j.arthro.2017.06.023 | DOI Listing |
JAMA Netw Open
January 2025
Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Importance: Secondary lymphedema is a common, harmful side effect of breast cancer treatment. Robust risk models that are externally validated are needed to facilitate clinical translation. A published risk model used 5 accessible clinical factors to predict the development of breast cancer-related lymphedema; this model included a patient's mammographic breast density as a novel predictive factor.
View Article and Find Full Text PDFArch Pathol Lab Med
January 2025
the Department of Pathology, The Ohio State University, Columbus (Parwani).
Context.—: Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities.
Objective.
Med Biol Eng Comput
January 2025
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, 215613, China.
Ultrasound blood flow imaging plays a crucial role in the diagnosis of cardiovascular and cerebrovascular diseases. Conventional ultrafast ultrasound plane-wave imaging techniques have limited capabilities in microvascular imaging. To enhance the quality of blood flow imaging, this study proposes a microbubble-based H-Scan ultrasound imaging technique.
View Article and Find Full Text PDFObjectives: To determine the value of preoperative magnetic resonance imaging (MRI) in predicting macrotrabecular-massive hepatocellular carcinoma (MTM-HCC).
Materials And Methods: A search was conducted on PubMed, Web of Science, Cochrane Library databases, and Embase for studies evaluating the performance of MRI in assessing MTM-HCC. The quality assessment of diagnostic studies (QUADAS-2) tool was used to assess the risk of bias.
Online J Public Health Inform
January 2025
Bureau of Communicable Disease, New York City Department of Health and Mental Hygiene, Long Island City, NY, United States.
Background: Applying nowcasting methods to partially accrued reportable disease data can help policymakers interpret recent epidemic trends despite data lags and quickly identify and remediate health inequities. During the 2022 mpox outbreak in New York City, we applied Nowcasting by Bayesian Smoothing (NobBS) to estimate recent cases, citywide and stratified by race or ethnicity (Black or African American, Hispanic or Latino, and White). However, in real time, it was unclear if the estimates were accurate.
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