Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
PloS one
Self-positioning in space science communication: A corpus-assisted discourse study
This study incorporates text mining into critical discourse analysis to examine how government science agencies in China and the United States position themselves in space science communication on social media. Two specialized corpora have been built by collecting posts from government science agencies on Weibo from China and X (formerly Twitter) from the U.S. With the help of the text mining tool KH Coder, this study gives a corpus-assisted discourse analysis of the particular ways of positioning at different levels of discourse: (1) topics/themes, (2) addressing terms, and (3) those words that co-occur with self-addressing terms in a sentence. The findings reveal significant differences in their preferential ways of positioning. Chinese government science agencies present themselves as state-affiliated yet approachable institutions, blending achievements and operational efficiency with patriotism and collective pride. Their use of diverse addressing terms and co-occurrence patterns portrays them as experienced, supportive guides, balancing national identity with interpersonal closeness. In contrast, U.S. government science agencies emphasize professionalism, focusing on research, space exploration, mission execution, and audience engagement, with minimal reference to state affiliation. Their addressing terms are formal and standardized, with pride centred on mission success and discovery, highlighting expertise and scientific leadership. Their preferential ways of positioning are further explained in their respective contexts in order to present a proper understanding of these differences.
12 July 2026
Read appraisal →Journal of Korean medical science
Evaluating Large Language Models for Post-Publication Promotion: A Blinded Comparative Study of Social Media Posts in Public Health
BACKGROUND: Social media platforms such as X (formerly Twitter) are increasingly used by journals, authors, and institutions to promote newly published research. Well-designed posts can enhance visibility, accelerate knowledge translation, and increase altmetric attention. However, creating accurate and policy-compliant content is time-intensive. Large language models (LLMs) offer a potential solution, yet systematic evaluations of their performance in post-publication promotion remain limited. METHODS: We conducted a blinded, crossed, offline evaluation of four LLMs: GPT-5 (OpenAI), Gemini 2.5 Pro (Google DeepMind), Grok-3 (xAI), and Perplexity Pro (Perplexity AI), tasked with generating X-style posts (≤ 260 characters) for 36 open access articles from The Lancet Public Health, The Lancet Planetary Health, and Annual Review of Public Health. Posts were generated using a standardized system and user prompt. A single blinded rater scored outputs using a five-domain rubric (factual accuracy, clarity, policy compliance, call-to-action quality, structure/metadata; maximum score 10). Secondary measures included character count, hashtag use, and readability (Flesch-Kincaid Grade Level). General linear models with Bonferroni-adjusted post hoc tests and non-parametric analyses were applied. RESULTS: All four models achieved perfect factual accuracy and no policy violations. Mean total quality scores differed significantly by model, P < 0.001. GPT-5 (9.60) and Perplexity Pro (9.60) performed best, followed by Gemini 2.5 Pro (9.47), while Grok-3 scored lower (8.80). Domain analyses showed Grok-3 underperformed in call-to-action quality (1.40 vs. ≥1.97 in other models, P < 0.001) and produced significantly shorter posts (median 194 characters, P < 0.001). Perplexity Pro scored highest for policy compliance, while GPT-5 and Gemini 2.5 Pro achieved superior structural scores. Readability varied: GPT-5 8.9 (7.3-9.2) and Perplexity Pro 7.3 (6.5-8.8) generated more complex outputs, whereas Gemini 2.5 Pro 5.1 (4.8-6.5) and Grok-3 4.5 (3.6-6.3) produced more accessible posts. CONCLUSION: LLMs can reliably generate accurate and policy-compliant social media posts for research promotion, with differences in style and readability that may inform audience targeting. GPT-5, Gemini 2.5 Pro, and Perplexity Pro produced high-quality outputs, while Grok-3 underperformed across several domains. These findings highlight the potential of LLMs as scalable first-draft tools for post-publication promotion, capable of improving the reach and accessibility of scientific research. Careful model selection, tailored to audience and communication goals, together with human oversight, remains essential.
25 June 2026
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