ObjectiveTo evaluate whether and to what extent the new risk of bias (ROB) tool has been used in Cochrane systematic reviews (CSRs) on acupuncture. MethodsWe searched the Cochrane Database of Systematic Review (CDSR) in issue 12, 2011. Two reviewers independently selected CSRs which primarily focused on acupuncture and moxibustion. Then the data involving in essential information, the information about ROB (sequence generation, allocation concealment, blindness, incomplete outcome data, selective reporting and other potential sources of bias) and GRADE were extracted and statistically analyzed. ResultsIn total, 41CSRs were identified, of which 19 CSRs were updated reviews. Thirty-three were published between 2009 and 2011. 60.98% reviews used the Cochrane Handbook as their ROB assessment tool. Most CSRs gave information about sequence generation, allocation concealment, blindness, and incomplete outcome data, however, half of them (54.55%, 8/69) showed selective reporting or other potential sources of bias. Conclusion"Risk of bias" tools have been used in most CSRs on acupuncture since 2009. However, the lack of evaluation items still remains.
The COSMIN community updated the COSMIN-RoB checklist on reliability and measurement error in 2021. The updated checklist can be applied to the assessment of all types of outcome measurement studies, including clinician-reported outcome measures (ClinPOMs), performance-basd outcome measurement instruments (PerFOMs), and laboratory values. In order to help readers better understand and apply the updated COSMIN-RoB checklist and provide methodological references for conducting systematic reviews of ClinPOMs, PerFOMs and laboratory values, this paper aimed to interpret the updated COSMIN-RoB checklist on reliability and measurement error studies.
ObjectiveTo interpret ROBIS, a new tool to evaluate the risk of bias in systematic reviews, to promote the comprehension of it and its proper application. MethodsWe explained each item of ROBIS tool, used it to evaluate the risk of bias of a selected intervention review whose title was Cyclophosphamide for Primary Nephrotic Syndrome of Children: A Systematic Review, and judged the risk of bias in the review. ResultsThe selected systematic review as a whole was rated as “high risk of bias”, because there existed high risk of bias in domain 2 to 4, namely identification and selection of studies, data collection and study appraisal, synthesis and findings. The risk of bias in domain 1 (study eligibility criteria) was low. The relevance of identified studies and the review’s research question was appropriately considered and the reviewers avoided emphasizing results on the basis of their statistical significance. ConclusionROBIS is a new tool worthy of being recommended to evaluate risk of bias in systematic reviews. Reviewers should use ROBIS items as standards to conduct and produce high quality systematic reviews.
With the rapid development of artificial intelligence (AI) and machine learning technologies, the development of AI-based prediction models has become increasingly prevalent in the medical field. However, the PROBAST tool, which is used to evaluate prediction models, has shown growing limitations when assessing models built on AI technologies. Therefore, Moons and colleagues updated and expanded PROBAST to develop the PROBAST+AI tool. This tool is suitable for evaluating prediction model studies based on both artificial intelligence methods and regression methods. It covers four domains: participants and data sources, predictors, outcomes, and analysis, allowing for systematic assessment of quality in model development, risk of bias in model evaluation, and applicability. This article interprets the content and evaluation process of the PROBAST+AI tool, aiming to provide references and guidance for domestic researchers using this tool.
Objective To systematically review the accuracy and consistency of large language models (LLM) in assessing risk of bias in analytical studies. Methods The cohort and case-control studies related to COVID-19 based on the team's published systematic review of clinical characteristics of COVID-19 were included. Two researchers independently screened the studies, extracted data, and assessed risk of bias of the included studies with the LLM-based BiasBee model (version Non-RCT) used for automated evaluation. Kappa statistics and score differences were used to analyze the agreement between LLM and human evaluations, with subgroup analysis for Chinese and English studies. Results A total of 210 studies were included. Meta-analysis showed that LLM scores were generally higher than those of human evaluators, particularly in representativeness of exposed cohorts (△=0.764) and selection of external controls (△=0.109). Kappa analysis indicated slight agreement in items such as exposure assessment (κ=0.059) and adequacy of follow-up (κ=0.093), while showing significant discrepancies in more subjective items, such as control selection (κ=−0.112) and non-response rate (κ=−0.115). Subgroup analysis revealed higher scoring consistency for LLM in English-language studies compared to that of Chinese-language studies. Conclusion LLM demonstrate potential in risk of bias assessment; however, notable differences remain in more subjective tasks. Future research should focus on optimizing prompt engineering and model fine-tuning to enhance LLM accuracy and consistency in complex tasks.
The QUADAS-2, QUIPS, and PROBAST tools are not specific for prognostic accuracy studies and the use of these tools to assess the risk of bias in prognostic accuracy studies is prone to bias. Therefore, QUAPAS, a risk of bias assessment tool for prognostic accuracy studies, has recently been developed. The tool combines QUADAS-2, QUIPS, and PROBAST, and consists of 5 domains, 18 signaling questions, 5 risk of bias questions, and 4 applicability questions. This paper will introduce the content and usage of QUAPAS to provide inspiration and references for domestic researchers.
The current issue of air pollution has pushed the development of the corresponding observational air pollution studies. The World Health Organization has developed a new risk of bias (RoB) assessment instrument and a related guideline for assessing the risk of potential bias in observational air pollution studies. This study introduced the background, methods, uses, advantages and disadvantages, precautions, and usage scenarios of the RoB instrument. It is expected to provide researchers with corresponding quality evaluation tools when writing related systematic review and meta-analysis, which will also help provide reporting standards for observational air pollution studies, thereby improving the quality of studies.
Nonrandomized studies are an important method for evaluating the effects of exposures (including environmental, occupational, and behavioral exposures) on human health. Risk of bias in nonrandomized studies of exposures (ROBINS-E) is used to evaluate the risk of bias in natural or occupational exposure observational studies. This paper introduces the main contents of ROBINS-E 2022, including backgrounds, seven domains, signal questions and the operation process.
This paper introduces the main contents of ROB-ME (Risk Of Bias due to Missing Evidence), including backgrounds, scope of the tool, signal questions and the operation process. The ROB-ME tool has the advantages of clear logic, complete details, simple operation and good applicability. The ROB-ME tool offers considerable advantages for assessing the risk of non-reporting biases and will be useful to researchers, thus being worth popularizing and applying.
At present, there are many items/checklists used to assess the methodological quality of animal studies. Yet, no tool has been specifically designed for assessing internal validity of animal studies. This articles introduce and interprets SYRCLE's risk of bias tool for animal studies in detail for Chinese scholars to accurately assess the methodological quality of animal studies when they develop systematic reviews on animal studies, so as to provide references for scientific design and implementation of animal studies.