Objectives To evaluate the reporting quality of Bland-Altman method consistency evaluation in China from 2014 to 2016. Methods WanFang Data, VIP and CNKI databases were electronically searched to collect literature about the application of Bland-Altman method from 2014 to 2016 in China. Two reviewers screened literature, extracted data, and the data were then statistically analyzed by SPSS 22.0 software. Results A total of 376 articles were included. The published articles on Bland-Altman method had major flaws (not conforming to reporting standards) in the application conditions, evaluation indexes, graphic depiction and so on. Merely 11.4% of the literature set the clinically acceptable consensus values in the pre-period studies. Merely one literature (0.3%) correctly compared the 95%CI of 95%LoA with the clinically acceptable threshold which had been set previously. The offer rates of the differences between the two measurements and the 95%CI, 95%LoA and 95%CI of 95%LoA in the figure were 95.9%, 9.5%, 94.6% and 4.4%, respectively. Conclusions The reporting quality of Bland-Altman method consistency evaluation in China is of low quality, specifically not conforming to reporting standards. We should strengthen the introduction of Bland-Altman methodology to improve the reporting quality.
ObjectiveTo integrate person imagery from drawing tests in screening for mental disorders through meta-analysis to identify indicators that can effectively predict mental disorders. MethodsA computerized search of CNKI, WanFang Data, VIP, PubMed, Web of Science, and EBSCO databases was conducted to collect studies related to mental disorders and drawing tests, with a search timeframe of the period from the creation of the database to May 8, 2023. Meta-analysis was performed using CMA 3.0 after two researchers independently screened the literature, extracted information, and assessed the risk of bias. ResultsA total of 43 studies were included, with 791 independent effect sizes and 8 444 subjects. Meta-analysis revealed that a total of 29 person imagery traits significantly predicted mental disorders, which could be categorized into 7 types according to the features: absent, bizarre, blackened, simplified, static, detailed, and holistic. The subgroup analysis revealed that the specific indicators of affective disorders included "excessive separation among items", "oversimplified person", "rigid and static person" and "hands behind the back". The specific indicators of thought disorders were "absence of limbs", "absence of facial features", and "disproportionate body proportions". Moreover, there were seven common indicators of mental disorders, including “oversimplified drawing”, “very small drawing”, “very small person”, “weak or intermittent lines”, “single line limb”, “absence of hands or feet”, and “no expression or dullness”. ConclusionThe findings could provide a reference standard for selection and interpretation of drawing indicators, promote standardization of the drawing test, and enhance the accuracy of results in screening for mental disorders.