Background: The digital landscape has become a vital platform for public health discourse, particularly concerning important topics like organ donation. With a global rise in organ transplant needs, fostering public understanding and positive attitudes toward organ donation is critical. Social media platforms, such as X, contain conversations from the public, and key stakeholders maintain an active presence on the platform.
Objective: The goal is to develop insights into organ donation discussions on a popular social media platform (X) and understand the context in which users discussed organ donation advocacy. We investigate the influence of prominent profiles on X and meta-level accounts, including those seeking health information. We use credibility theory to explore the construction and impact of credibility within social media contexts in organ donation discussions.
Methods: Data were retrieved from X between October 2023 and May 2024, covering a 7-month period. The study was able to retrieve a dataset with 20,124 unique users and 33,830 posts. The posts were analyzed using social network analysis and qualitative thematic analysis. NodeXL Pro was used to retrieve and analyze the data, and a network visualization was created by drawing upon the Clauset-Newman-Moore cluster algorithm and the Harel-Koren Fast Multiscale layout algorithm.
Results: This analysis reveals an "elite tier" shaping the conversation, with themes reflecting existing societal sensitivities around organ donation. We demonstrate how prominent social media profiles act as information intermediaries, navigating the tension between open dialogue and negative perceptions. We use our findings, social credibility theory, and review of existing literature to develop the OrgReach Social Media Marketing Strategy for Organ Donation Awareness. The OrgReach strategy developed is based on 5 C's (Create, Connect, Collaborate, Correct, and Curate), 2 A's (Access and Analyse), and 3 R's (Recognize, Respond, and Reevaluate).
Conclusions: The study highlights the crucial role of analyzing social media data by drawing upon social networks and topic analysis to understand influence and network communication patterns. By doing so, the study proposes the OrgReach strategy that can feed into the marketing strategies for organ donation outreach and awareness.
Background: Alzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. Vision transformers (ViTs) are emerging as promising deep learning models in medical imaging, with potential applications in the detection and diagnosis of AD.
Objective: This review systematically examines recent studies on the application of ViTs in detecting AD, evaluating the diagnostic accuracy and impact of network architecture on model performance.
Methods: We conducted a systematic search across major medical databases, including China National Knowledge Infrastructure, CENTRAL (Cochrane Central Register of Controlled Trials), ScienceDirect, PubMed, Web of Science, and Scopus, covering publications from January 1, 2020, to March 1, 2024. A manual search was also performed to include relevant gray literature. The included papers used ViT models for AD detection versus healthy controls based on neuroimaging data, and the included studies used magnetic resonance imaging and positron emission tomography. Pooled diagnostic accuracy estimates, including sensitivity, specificity, likelihood ratios, and diagnostic odds ratios, were derived using random-effects models. Subgroup analyses comparing the diagnostic performance of different ViT network architectures were performed.
Results: The meta-analysis, encompassing 11 studies with 95% CIs and P values, demonstrated pooled diagnostic accuracy: sensitivity 0.925 (95% CI 0.892-0.959; P<.01), specificity 0.957 (95% CI 0.932-0.981; P<.01), positive likelihood ratio 21.84 (95% CI 12.26-38.91; P<.01), and negative likelihood ratio 0.08 (95% CI 0.05-0.14; P<.01). The area under the curve was notably high at 0.924. The findings highlight the potential of ViTs as effective tools for early and accurate AD diagnosis, offering insights for future neuroimaging-based diagnostic approaches.
Conclusions: This systematic review provides valuable evidence for the utility of ViT models in distinguishing patients with AD from healthy controls, thereby contributing to advancements in neuroimaging-based diagnostic methodologies.
Trial registration: PROSPERO CRD42024584347; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=584347.
Implementation of patient-reported outcome measures (PROMs) in clinical practice is challenging. We believe effective communication is key to realizing the clinical benefits of PROMs. Communication processes for PROMs in clinical practice typically involve (1) health care professionals (HCPs) inviting patients to complete PROMs, (2) patients completing PROMs, (3) HCPs and patients interpreting the resulting patient-reported outcomes (PROs), and (4) HCPs and patients using PROs for health management. Yet, communication around PROMs remains underexplored. Importantly, patients differ in their skills, knowledge, preferences, and motivations for completing PROMs, as well as in their ability and willingness to interpret and apply PROs in managing their health. Despite this, current communication practices often fail to account for these differences. This paper highlights the importance of personalized communication to make PROMs accessible to diverse populations. Personalizing communication manually is highly labor-intensive, but several digital technologies can offer a feasible solution to accommodate various patients. Despite their potential, these technologies have not yet been applied to PROMs. We explore how existing principles and tools, such as automatic data-to-text generation (including multimodal outputs like text combined with data visualizations) and conversational agents, can enable personalized communication of PROMs in practice.
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