Background The health effects of dietary fats are a controversial issue on which experts and authoritative organizations have often disagreed. Care providers, guideline developers, policy-makers, and researchers use systematic reviews to advise patients and members of the public on optimal dietary habits, and to formulate public health recommendations and policies. Existing reviews, however, have serious limitations that impede optimal dietary fat recommendations, such as a lack of focus on outcomes important to people, substantial risk of bias (RoB) issues, ignoring absolute estimates of effects together with comprehensive assessments of the certainty of the estimates for all outcomes.Objective We therefore propose a methodologically innovative systematic review using direct and indirect evidence on diet and food-based fats (i.e., reduction or replacement of saturated fat with monounsaturated or polyunsaturated fat, or carbohydrates or protein) and the risk of important health outcomes.Methods We will collaborate with an experienced research librarian to search MEDLINE, EMBASE, CINAHL, and the Cochrane Database of Systematic Reviews (CDSR) for randomized clinical trials (RCTs) addressing saturated fat and our health outcomes of interest. In duplicate, we will screen, extract results from primary studies, assess their RoB, conduct de novo meta-analyses and/or network meta-analysis, assess the impact of missing outcome data on meta-analyses, present absolute effect estimates, and assess the certainty of evidence for each outcome using the GRADE contextualized approach. Our work will inform recommendations on saturated fat based on international standards for reporting systematic reviews and guidelines.Conclusion Our systematic review and meta-analysis will provide the most comprehensive and rigorous summary of the evidence addressing the relationship between saturated fat modification for people-important health outcomes. The evidence from this review will be used to inform public health nutrition guidelines.

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: 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.

OBJECTIVE To compare the effects of treatments for coronavirus disease 2019 (covid-19). DESIGN Living systematic review and network meta-analysis. DATA SOURCES US Centers for Disease Control and Prevention COVID-19 Research Articles Downloadable Database, which includes 25 electronic databases and six additional Chinese databases to 20 July 2020. STUDY SELECTION Randomised clinical trials in which people with suspected, probable, or confirmed covid-19 were randomised to drug treatment or to standard care or placebo. Pairs of reviewers independently screened potentially eligible articles. METHODS After duplicate data abstraction, a bayesian random effects network meta-analysis was conducted. Risk of bias of the included studies was assessed using a modification of the Cochrane risk of bias 2.0 tool, and the certainty of the evidence using the grading of recommendations assessment, development and evaluation (GRADE) approach. For each outcome, interventions were classified in groups from the most to the least beneficial or harmful following GRADE guidance. RESULTS 23 randomised controlled trials were included in the analysis performed on 26 June 2020. The certainty of the evidence for most comparisons was very low because of risk of bias (lack of blinding) and serious imprecision. Glucocorticoids were the only intervention with evidence for a reduction in death compared with standard care (risk difference 37 fewer per 1000 patients, 95% credible interval 63 fewer to 11 fewer, moderate certainty) and mechanical ventilation (31 fewer per 1000 patients, 47 fewer to 9 fewer, moderate certainty). These estimates are based on direct evidence; network estimates for glucocorticoids compared with standard care were less precise because of network heterogeneity. Three drugs might reduce symptom duration compared with standard care: hydroxychloroquine (mean difference -4.5 days, low certainty), remdesivir (-2.6 days, moderate certainty), and lopinavir-ritonavir (-1.2 days, low certainty). Hydroxychloroquine might increase the risk of adverse events compared with the other interventions, and remdesivir probably does not substantially increase the risk of adverse effects leading to drug discontinuation. No other interventions included enough patients to meaningfully interpret adverse effects leading to drug discontinuation. CONCLUSION Glucocorticoids probably reduce mortality and mechanical ventilation in patients with covid-19 compared with standard care. The effectiveness of most interventions is uncertain because most of the randomised controlled trials so far have been small and have important study limitations.