Background There are debates in acupuncture related systematic reviews and meta-analyses on whether searching Chinese databases to get more Chinese-language studies may increase the risk of bias and overestimate the effect size, and whether the treatment effects of acupuncture differ between Chinese and non-Chinese populations.Methods In this meta-epidemiological study, we searched the Cochrane library from its inception until December 2021, and identified systematic reviews and meta-analyses with acupuncture as one of the interventions. Paired reviewers independently screened the reviews and extracted the information. We repeated the meta-analysis of the selected outcomes to separately pool the results of Chinese- and non-Chinese-language acupuncture studies and presented the pooled estimates as odds ratios (OR) with 95% confidence interval (CI). We calculated the Ratio of ORs (ROR) by dividing the OR of the Chinese-language trials by the OR of the non-Chinese-language trials, and the ROR by dividing the OR of trials addressing Chinese population by the OR of trials addressing non-Chinese population. We explored whether the impact of a high risk of bias on the effect size differed between studies published in Chinese- and in non-Chinese-language, and whether the treatment effects of acupuncture differed between Chinese and non-Chinese population.Results We identified 84 Cochrane acupuncture reviews involving 33 Cochrane groups, of which 31 reviews (37%) searched Chinese databases. Searching versus not searching Chinese databases significantly increased the contribution of Chinese-language literature both to the total number of included trials (54% vs. 15%) and the sample size (40% vs. 15%). When compared with non-Chinese-language trials, Chinese-language trials were associated with a larger effect size (pooled ROR 0.51, 95% CI 0.29 to 0.91). We also observed a higher risk of bias in Chinese-language trials in blinding of participants and personnel (97% vs. 51%) and blinding of outcome assessment (93% vs. 47%). The higher risk of bias was associated with a larger effect estimate in both Chinese-language (allocation concealment: high/unclear risk vs. low risk, ROR 0.43, 95% CI 0.21 to 0.87) and non-Chinese-language studies (blinding of participants and personnel: high/unclear risk vs. low risk, ROR 0.41, 95% CI 0.23 to 0.74). However, we found no evidence that the higher risk of bias would increase the effect size of acupuncture in Chinese-language studies more often than in non-Chinese-language studies (the confidence intervals of all ROR in the high-risk group included 1, Table 3). We further found acupuncture appeared to be more effective in Chinese than in non-Chinese population (Table 4).Conclusions The findings of this study suggest the higher risk of bias may lead to an overestimation of the treatment effects of acupuncture but would not increase the treatment effects in Chinese-language studies more often than in other language studies. The difference in treatment effects of acupuncture was probably associated with differences in population characteristics.

IntroductionThere was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning-based prediction model in preterm birth. Material and methodsWe conducted a systematic review, searching the PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disk, VIP Database, and WanFang Data from inception to September 27, 2021. Studies that developed (validated) a prediction model using machine learning methods in preterm birth were included. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and Prediction model Risk of Bias Assessment Tool (PROBAST) to evaluate the reporting quality and the risk of bias of included studies, respectively. Findings were summarized using descriptive statistics and visual plots. The protocol was registered in PROSPERO (no. CRD 42022301623). ResultsTwenty-nine studies met the inclusion criteria, with 24 development-only studies and 5 development-with-validation studies. Overall, TRIPOD adherence per study ranged from 17% to 79%, with a median adherence of 49%. The reporting of title, abstract, blinding of predictors, sample size justification, explanation of model, and model performance were mostly poor, with TRIPOD adherence ranging from 4% to 17%. For all included studies, 79% had a high overall risk of bias, and 21% had an unclear overall risk of bias. The analysis domain was most commonly rated as high risk of bias in included studies, mainly as a result of small effective sample size, selection of predictors based on univariable analysis, and lack of calibration evaluation. ConclusionsReporting and methodological quality of machine learning-based prediction models in preterm birth were poor. It is urgent to improve the design, conduct, and reporting of such studies to boost the application of machine learning-based prediction models in preterm birth in clinical practice.

Objectives: To establish whether items included in instruments published in the last decade assessing risk of bias of randomized controlled trials (RCTs) are indeed addressing risk of bias.Study Design and Setting: We searched Medline, Embase, Web of Science, and Scopus from 2010 to October 2021 for instruments assessing risk of bias of RCTs. By extracting items and summarizing their essential content, we generated an item list. Items that two re-viewers agreed clearly did not address risk of bias were excluded. We included the remaining items in a survey in which 13 experts judged the issue each item is addressing: risk of bias, applicability, random error, reporting quality, or none of the above.Results: Seventeen eligible instruments included 127 unique items. After excluding 61 items deemed as clearly not addressing risk of bias, the item classification survey included 66 items, of which the majority of respondents deemed 20 items (30.3%) as addressing risk of bias; the majority deemed 11 (16.7%) as not addressing risk of bias; and there proved substantial disagreement for 35 (53.0%) items. Conclusion: Existing risk of bias instruments frequently include items that do not address risk of bias. For many items, experts disagree on whether or not they are addressing risk of bias.(c) 2022 Elsevier Inc. All rights reserved.

Objective: This study collected randomized controlled trials (RCTs) published in the social sciences in China and assessed their risk of bias and reporting quality. Study design and setting: Three databases were systematically searched for publications from January 2000 to June 2020 for RCTs in the social sciences published by Chinese researchers. The risk of bias was assessed using the Cochrane Collaboration Risk of Bias Tool, and reporting quality was assessed using the Consolidated Standards of Reporting Trials for Social and Psychological Interventions (CONSORT-SPI). Results: A total of 316 RCTs were identified, including 204 articles in English and 112 articles in Chinese. The most frequently researched interventions focused on education (33.9%), and the most frequently studied population were students (32.9%). Eighty-seven percent of RCTs had intermediate reporting quality. Twenty-four of the 43 CONSORT-SPI sub-items had a compliance rate of less than 50%. Most RCTs had an unclear risk of bias for blinding outcome assessors (84.5%), blinding participants and personnel (82.9%), allocation concealment (73.1%), and random sequence generation (68.0%). A low proportion of CONSORT-SPI items were reported and, high proportion of the papers had unclear risk of bias. Conclusion: The quality and reporting of RCTs in the social sciences needs improvement in China, especially for reporting methods and results. Most studies had an unclear risk of bias as they lacked important methodological information. (c) 2021 Elsevier Inc. All rights reserved.

Objective: To describe the characteristics of Covid-19 randomized clinical trials (RCTs) and examine the association between trial characteristics and the likelihood of finding a significant effect. Study design: We conducted a systematic review to identify RCTs (up to October 21, 2020) evaluating drugs or blood products to treat or prevent Covid-19. We extracted trial characteristics (number of centers, funding sources, and sample size) and assessed risk of bias (RoB) using the Cochrane RoB 2.0 tool. We performed logistic regressions to evaluate the association between RoB due to randomization, single vs. multicentre, funding source, and sample size, and finding a statistically significant effect. Results: We included 91 RCTs (n = 46,802); 40 (44%) were single-center, 23 (25.3%) enrolled < 50 patients, 28 (30.8%) received industry funding, and 75 (82.4%) had high or probably high RoB. Thirty-eight trials (41.8%) reported a statistically significant effect. RoB due to randomization and being a single-center trial were associated with increased odds of finding a statistically significant effect. Conclusions: There is high variability in RoB among Covid-19 trials. Researchers, funders, and knowledge-users should be cognizant of the impact of RoB due to randomization and single-center trial status in designing, evaluating, and interpreting the results of RCTs. (C) 2021 Elsevier Inc. All rights reserved.