Characteristics and Research Waste Among Randomized Clinical Trials in Gastric Cancer

Lü, Jun; Xu, Binbin; Song, Lili; Wu, Dong; Zhang, Xue; Zheng, Hua-Long; Xie, Jian‐Wei; Wang, Jia-Bin; Ji, Lin; Chen, Qi‐Yue; Cao, Long‐Long; Ma, Lin; Tu, Ru‐Hong; Huang, Ze-Ning; Lin, Ju‐Li; Huang, Chang‐Ming; Zheng, Chao‐Hui; Li, Ping

doi:10.1001/jamanetworkopen.2021.24760

Cited by 9 publications

(3 citation statements)

References 42 publications

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“…Regarding the PRISMA 2009 statement, items two, five, eight, nine, 17, 22, and 24 need significant improvement. In recent years, the problem of reproducibility and the waste of resources in biomedical research have caused considerable concern in the scientific community ( 50 , 51 ). However, comprehensive and transparent reporting of the study design, study process, and final outcomes is key to avoiding these problems.…”

Section: Discussionmentioning

confidence: 99%

Nigella sativa and health outcomes: An overview of systematic reviews and meta-analyses

Wang

et al. 2023

Front. Nutr.

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BackgroundNigella sativa (N. sativa) consumption has been associated with various health outcomes; however, the results are not completely consistent.ObjectivesThis overview of systematic reviews and meta-analyses aimed to evaluate the reporting and methodological quality, and to grade the available evidence of associations between N. sativa and health outcomes.MethodsPubMed, Cochrane Library, Embase, and Scopus databases were searched from their inception to September 30, 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 statement, Assessment of Multiple Systematic Reviews (AMSTAR) 2 checklist, and Grades of Recommendations, Assessment, Development and Evaluations (GRADE) systems were used to assess the reporting, methodological, and evidence quality for each meta-analysis, respectively. The results were synthesized in a narrative form.ResultsThis overview included 20 eligible meta-analyses published in peer-reviewed journals between 2013 and 2021. The overall methodological quality was relatively poor, with only one moderate quality, four low quality, and 15 critically low quality studies. For reporting quality, items two, five, eight, nine, 15, and 24 need to improve. Among the 110 outcome indicators of the quality of evidence, five were graded as moderate, 17 as low, and 88 as very low. Risk of bias, inconsistency, and imprecision were the main downgrading factors.ConclusionThis overview suggests that N. sativa is beneficial for various clinical outcomes. However, there are certain limitations to reporting and methodological quality. The clinical efficacy of N. sativa requires confirmation in high-quality, large-sample, randomized controlled trials.

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Section: Discussionmentioning

confidence: 99%

Nigella sativa and health outcomes: An overview of systematic reviews and meta-analyses

Wang

et al. 2023

Front. Nutr.

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“…Several previous studies have also validated our findings. In the research of characteristics among randomized controlled trials (RCTs) in gastric cancer, Lu et al noted that RCTs conducted in non-Asian regions (OR = 2.76; 95% CI: 1.03–7.35) or with multicenter designs (OR = 3.84; 95% CI: 1.58–9.38) or external funds (OR = 2.373; 95% CI: 1.05–5.38) were more likely to publish their outcomes [ 58 ]. A retrospective study of the literature by Canestaro et al also found that trials with multicenter designs (OR = 1.32; 95% CI: 1.16–1.45) were associated with study results publication [ 59 ].…”

Section: Discussionmentioning

confidence: 99%

Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov

Wang

Xiu

Liu

et al. 2022

IJERPH

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Artificial intelligence (AI) has driven innovative transformation in healthcare service patterns, despite a lack of understanding of its performance in clinical practice. We conducted a cross-sectional analysis of AI-related trials in healthcare based on ClinicalTrials.gov, intending to investigate the trial characteristics and AI’s development status. Additionally, the Neo4j graph database and visualization technology were employed to construct an AI technology application graph, achieving a visual representation and analysis of research hotspots in healthcare AI. A total of 1725 eligible trials that were registered in ClinicalTrials.gov up to 31 March 2022 were included in this study. The number of trial registrations has dramatically grown each year since 2016. However, the AI-related trials had some design drawbacks and problems with poor-quality result reporting. The proportion of trials with prospective and randomized designs was insufficient, and most studies did not report results upon completion. Currently, most healthcare AI application studies are based on data-driven learning algorithms, covering various disease areas and healthcare scenarios. As few studies have publicly reported results on ClinicalTrials.gov, there is not enough evidence to support an assessment of AI’s actual performance. The widespread implementation of AI technology in healthcare still faces many challenges and requires more high-quality prospective clinical validation.

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“…Research waste stems from research with little to no societal, educational or stakeholder benefit [ 4 ]. Poorly reported or unpublished results compound research waste, pose a risk to the care of future patients and present ethical concerns [ 5 , 12 ]. Although research waste is not a new problem, it was accelerated by the COVID-19 pandemic and was prevalent across trials, evidence synthesis and guidelines [ 2 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring trial publication and research waste in COVID-19 randomised trials of hydroxychloroquine, corticosteroids, and vitamin D: a meta-epidemiological cohort study

Fincham,

Hohlfeld,

Clarke

et al. 2024

BMC Med Res Methodol

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Background The global research response to the COVID-19 pandemic was impressive, but also led to an infodemic and considerable research waste. Registered, but unpublished trials added to this noise. We aimed to determine the proportion of registered randomised trials of common COVID-19 treatments that were published and to describe the characteristics of these trials to examine the association between trial characteristics, publication status and research waste. Methods This meta-epidemiological cohort study used a sample of randomised trials of corticosteroids, hydroxychloroquine or vitamin D as treatments for COVID-19, registered between 1 November 2019 and 31 December 2021 and available via the WHO ICTRP portal. We searched for the trials’ published results up to 20 October 2022. We extracted the trial characteristics, analysing with descriptive statistics. We performed univariate logistic regression to examine the association between trials’ characteristics and publication status, followed by multiple logistic regression using significant characteristics to assess the association between trial characteristics and publication status. Results We identified 357 eligible trials on ICTRP. Of these, 107 (30%) had published or made their results available publicly by 20 October 2022, while 250 (70%) had not been published or shared their results publicly. Multiple logistic regression analysis showed that a larger target sample size was a significant positive predictor of publication with target sample sizes above 300 almost tripling the odds of publication (aOR: 2.75, 95% CI: 1.35 to 5.62). Conclusions Less than one third of registered trials made their results public and our findings identified that many trialists had not updated their trial registry entry with the trial status, results or both. Failure to share trial results publicly is a disservice to patients, clinicians and policy makers and adds to research waste.

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Characteristics and Research Waste Among Randomized Clinical Trials in Gastric Cancer

Abstract: This cross-sectional study examines the characteristics and presence of research waste, defined as unpublished data, inadequate reporting, or avoidable design limitations, among randomized clinical trials in gastric cancer.

Cited by 9 publications

References 42 publications

Nigella sativa and health outcomes: An overview of systematic reviews and meta-analyses

Nigella sativa and health outcomes: An overview of systematic reviews and meta-analyses

Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov

Exploring trial publication and research waste in COVID-19 randomised trials of hydroxychloroquine, corticosteroids, and vitamin D: a meta-epidemiological cohort study

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