Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 4 appraisals
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Patient experiences of tissue donation and digital consent support in primary craniospinal tumour research.
PURPOSE: Requests for tissue donation for research are often made at times of heightened vulnerability, particularly around diagnosis and surgery. This study explored patient experiences of tissue donation discussions, perspectives on consent, and the acceptability of digital decision support in primary craniospinal tumour research. METHODS: A UK national online cross-sectional survey was conducted with 50 adults with a primary brain tumour or spinal sarcoma. The survey was developed with patient and public involvement; six patient contributors reviewed the initial questionnaire before launch. Descriptive statistics summarised closed responses, and open-text comments were grouped descriptively to contextualise quantitative findings. Reporting was informed by STROBE guidance. RESULTS: Just over half of participants reported being invited to donate tissue for research (26/50, 52%). Respondents strongly preferred tissue donation to be discussed at or after a clinic appointment, and none selected the day of surgery as the preferred time. Among invited respondents, most reported that information was easy to understand (22/26, 85%), that they had an opportunity to ask questions (23/25, 92%), and that they had sufficient time to consider the decision (23/26, 88%). Sixteen of 26 invited respondents (62%) discussed the decision with family or friends; among invited respondents who had not done so, 7/10 (70%) would have liked the opportunity. Interest in a secure digital adjunct was high (46/49, 94%). CONCLUSION: Overall experience was generally positive, but the data identify specific, practical opportunities to strengthen consent support in rare craniospinal tumour pathways, including appropriate timing, clear and revisitable information, opportunities for question-asking, and resources that support family-inclusive decision-making.
19 July 2026
Read appraisal →Journal of global health
Animal-assisted therapy on psychological and physical outcomes: a meta-analysis of randomised controlled trials
BACKGROUND: Adults are at heightened risk of anxiety, stress, and depression; animal-assisted therapy (AAT) may serve as an effective approach to promote psychological well-being. In this study, we compared the effectiveness of AAT in improving depression, anxiety, stress, pain, and gait among adults with or without illness. METHODS: We systematically searched six electronic databases (PubMed, CINAHL, Embase, Web of Science, the Cochrane Library, and Scopus) and included all studies published up to August 2024. We used comprehensive meta-analysis software to complete the quantitative synthesis. We reported pooled effect sizes as Hedges' g with corresponding 95% confidence intervals (CIs), after applying a random-effects model. Furthermore, we assessed heterogeneity using Cochran's Q test and the I2 statistic. We applied the Cochrane Risk of Bias 2.0 tool to appraise the methodological quality of the included studies. The synthesis process followed PRISMA guidelines. RESULTS: From the 13,345 studies identified, 35 randomised controlled trials involving 2391 adults were included. Across diverse populations, AAT was associated with reductions in depression (Hedges' g = -0.403; 95% CI = -0.536, -0.271), anxiety (Hedges' g = -0.661; 95% CI = -1.069, -0.253), and stress (Hedges' g = -1.062; 95% CI = -1.849, -0.275) at post-intervention, although substantial between-study variability was observed. CONCLUSIONS: We demonstrated that AAT significantly improves anxiety, depression and stress in adults, but has no meaningful effect on pain or gait. Subgroup and meta-regression analyses indicate that psychological benefits depend on population and intervention characteristics, and are not moderated by age or gender. REGISTRATION: PROSPERO: CRD42024570108.
19 July 2026
Read appraisal →Journal of medical Internet research
Exploring the Narratives of Patients With Cancer Using Large Language Models: Topic Modeling and Social Network Analysis
BACKGROUND: Patients with cancer often experience diverse psychosocial stressors that profoundly affect disease trajectories, treatment adherence, and overall quality of life. Understanding how patients experience and articulate these issues is critical for designing patient-centered interventions. Conventional data collection methods, such as surveys and interviews, provide depth but are constrained by recall bias and scalability and may overlook sensitive or underreported concerns. Patient-authored narratives in online health communities present a valuable opportunity to identify prevalent and underserved issues. However, critical analytic challenges remain in generating coherent and interpretable insights due to their unstructured and large-scale nature. OBJECTIVE: This study aims to leverage TopicGPT, a prompt-based topic modeling framework powered by large language models (LLMs), in combination with network analysis for interpretable topic discovery and interrelationship analysis in the narratives of patients with cancer. METHODS: Patient-authored posts describing psychosocial challenges about cancer experience were collected from 4 online health communities. Eligible posts were preprocessed and analyzed using TopicGPT, wherein topics were generated hierarchically and mapped at the sentence level. Comparison analyses were conducted among 3 state-of-the-art LLMs through cosine similarity and manual evaluation. Results from the best-performing LLM were further compared with 2 conventional topic models through topic diversity and were used to construct the network subsequently. Topic co-occurrence was examined using the pointwise mutual information algorithm and centrality metrics to reveal influential topics and thematic interconnections across narratives. RESULTS: A total of 11,306 posts were collected from Reddit, Macmillan, Mijian, and Douban between December 6, 2006, and September 24, 2025. Of these, 3169 posts were retained for topic modeling and network analysis. DeepSeek-V3.2 consistently outperformed Gemini-2.5-Flash and GPT-4o, with similarity scores of 0.6295, 0.5342, and 0.5247, respectively. TopicGPT maintained consistently high topic diversity across languages. "Fear of cancer recurrence" and "Psychological distress" emerged as both most frequent and bridging topics across a hierarchy comprising 42 top-level and 58 subtopics. Strong connections were observed among "Sexual health concerns," "Reproductive concerns," and "Quality of life impact"; "Family communication concerns" frequently co-occurred with "Employment concerns," "Diagnostic delays and misdiagnosis," and "Social support." CONCLUSIONS: This study demonstrates the potential of LLM-based topic modeling for large-scale, context-sensitive analysis of patient-authored narratives. The proposed integrated, domain-adaptable pipeline enables the identification of high-fidelity topics and their interrelationships, offering a scalable and interpretable approach to qualitative data in health care. Importantly, our findings reveal substantial concerns and unmet needs among patients with cancer, with potential to support patient-centered research and inform future clinical assessment and supportive care strategies.
7 July 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
The effectiveness of non-pharmacological interventions on sleep quality in lung cancer patients: a systematic review and meta-analysis (2015-2025)
PURPOSE: Sleep disorders are highly prevalent among lung cancer patients and seriously affect their quality of life and mental health. This systematic review and meta-analysis aimed to assess the effects of non-pharmacological interventions on sleep quality, quality of life, and anxiety in lung cancer patients. METHODS: PubMed, Web of Science, Cochrane Library, CINAHL, Embase, CNKI, Wanfang Data, and VIP databases for studies published between 2015 and February 2025. The primary outcome was sleep quality, with quality of life and anxiety levels as secondary outcomes. We assessed the risk of bias using the Cochrane tool and synthesized data using meta-analysis. RESULTS: Twenty-four studies involving 2604 patients were included. Non-pharmacological interventions were compared with usual care, placebo, or standard oncology care. After excluding studies with inappropriate content and methods, 20 studies evaluated sleep quality, and 12 studies evaluated quality of life and anxiety levels, respectively. The quality of the evidence was low to moderate; the promotion results should be interpreted with caution, but they may have a positive effect on the sleep quality, quality of life, and anxiety levels of lung cancer patients. CONCLUSIONS: Diverse non-pharmacological interventions can improve sleep quality, quality of life, and anxiety in lung cancer patients. However, high-quality, rigorously designed randomized controlled trials are still needed to confirm these findings. Future research should focus on personalized interventions tailored to lung cancer patients' characteristics and integrate digital health interventions to better support clinical decision-making.
28 June 2026
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