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This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews. A machine learning classifier for retrieving randomized controlled trials (RCTs) was developed (the “Cochrane RCT Classifier”), with the algorithm trained using a data set of title–abstract records from Embase, manually labeled by the Cochrane Crowd. The classifier was then calibrated using a further data set of similar records manually labeled by the Clinical Hedges team, aiming for 99% recall. Finally, the recall of the calibrated classifier was evaluated using records of RCTs included in Cochrane Reviews that had abstracts of sufficient length to allow machine classification. The Cochrane RCT Classifier was trained using 280,620 records (20,454 of which reported RCTs). A classification threshold was set using 49,025 calibration records (1,587 of which reported RCTs), and our bootstrap validation found the classifier had recall of 0.99 (95% confidence interval 0.98–0.99) and precision of 0.08 (95% confidence interval 0.06–0.12) in this data set. The final, calibrated RCT classifier correctly retrieved 43,783 (99.5%) of 44,007 RCTs included in Cochrane Reviews but missed 224 (0.5%). Older records were more likely to be missed than those more recently published. The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane Reviews, with a very low and acceptable risk of missing eligible RCTs. This classifier now forms part of the Evidence Pipeline, an integrated workflow deployed within Cochrane to help improve the efficiency of the study identification processes that support systematic review production. •Systematic review processes need to become more efficient.•Machine learning is sufficiently mature for real-world use.•A machine learning classifier was built using data from Cochrane Crowd.•It was calibrated to achieve very high recall.•It is now live and in use in Cochrane review production systems.

Over the past 70 years, randomized, controlled trials (RCTs) have reshaped medical knowledge and practice. Popularized by mid-20th-century clinical researchers and statisticians aiming to reduce bias and enhance the accuracy of clinical experimentation, RCTs have often functioned well in that role. Yet the past seven decades also bear witness to many limitations of this new “gold standard.” The scientific and political history of RCTs offers lessons regarding the complexity of medicine and disease and the economic and political forces that shape the production and circulation of medical knowledge. The Rise of RCTs Physicians and medical researchers have attempted for millennia . . .

In evidence synthesis, assessing risk of bias (ROB) of eligible studies is crucial to inform interpretation of findings. Standardized tools like Cochrane's ROB-1 or ROB-2 traditionally rely on published information to inform assessments, but this is often incomplete or unclear. Availability of raw individual participant data (IPD) enables more in-depth assessments; however, guidance on how to use IPD in ROB assessments is lacking. We aim to develop preliminary guidance on how to use IPD to inform ROB assessments of randomized controlled trials (RCTs) for three case studies. In stage 1, we reviewed relevant literature, consulted our networks, and drew on previous experience to compile items on how IPD may inform ROB assessment for each domain. We discussed feasibility and potential usefulness of each item with an international, interdisciplinary expert advisory group and developed preliminary guidance, which was piloted in two IPD meta-analyses (MAs) (65 RCTs) using ROB-1. In stage 2, the guide was adapted for ROB-2 and applied to another IPD-MA (34 RCTs). All assessments were conducted in duplicate by two independent reviewers. In stage 3, we conducted an evaluation workshop to further refine each item, and capture important lessons. To assess the impact of IPD-informed assessments, we compared them to existing ROB-1 assessments performed with published information alone for 33 trials. We identified 12 items across the ROB domains. IPD provided opportunities to enhance ROB assessments by enabling additional checks for selection bias (ie, testing randomization) and attrition bias (ie, more granular assessment of incomplete data at various time points). We also identified domains for which availability of IPD enabled reduction of ROB, for instance, by mitigating selective outcome reporting bias or by reincluding excluded participants in intention-to-treat analyses. Applying IPD-informed assessments led to changes in ROB judgment in 25 of 33 studies, most commonly, resolution of domains previously marked as “unclear”. Our preliminary guidance for IPD-informed ROB assessments may be applied in IPD-MAs to increase the accuracy of ROB assessments and in some cases reduce ROB to create a more reliable evidence base informing policy and practice. When making decisions about how to treat a patient in clinical practice, it is important to consider the results of all relevant studies. Usually, combined analyses of multiple clinical trials rely on published reports, in which researchers summarize their findings. However, looking at the original data from these studies, instead of just the published reports, can improve the quality of analyses. Access to these underlying data also allows for more thorough assessment of the studies' quality and any potential for bias. This is important for understanding the results properly and for making the most appropriate treatment decisions for patients. Here, we present guidance on how to assess risk of bias of trials using these original datasets. [Display omitted] Key findings•Using Individual participant data (IPD) to inform risk of bias (ROB) assessments may reduce uncertainty, and in some cases reduce ROB.What this adds to what is known?•Standard methods of assessing ROB in a systematic review and meta-analysis (MA) rely on published information alone. IPD allow additional checks to be performed across several domains during ROB assessment.What is the implication, what should change now?•Our preliminary guidance for IPD-informed ROB assessments may be applied in IPD-MAs to create a more reliable evidence base informing policy and practice.

Aims/hypothesis The aims of the study were to evaluate the association between type 2 diabetes and the risk of death from any cancer and specific cancers in East and South Asians. Methods Pooled analyses were conducted of 19 prospective population-based cohorts included in the Asia Cohort Consortium, comprising data from 658,611 East Asians and 112,686 South Asians. HRs were used to compare individuals with diabetes at baseline with those without diabetes for the risk of death from any cancer and from site-specific cancers, including cancers of the oesophagus, stomach, colorectum, colon, rectum, liver, bile duct, pancreas, lung, breast, endometrium, cervix, ovary, prostate, bladder, kidney and thyroid, as well as lymphoma and leukaemia. Results During a mean follow-up of 12.7 years, 37,343 cancer deaths (36,667 in East Asians and 676 in South Asians) were identified. Baseline diabetes status was statistically significantly associated with an increased risk of death from any cancer (HR 1.26; 95% CI 1.21, 1.31). Significant positive associations with diabetes were observed for cancers of the colorectum (HR 1.41; 95% CI 1.26, 1.57), liver (HR 2.05; 95% CI 1.77, 2.38), bile duct (HR 1.41; 95% CI 1.04, 1.92), gallbladder (HR 1.33; 95% CI 1.10, 1.61), pancreas (HR 1.53; 95% CI 1.32, 1.77), breast (HR 1.72; 95% CI 1.34, 2.19), endometrium (HR 2.73; 95% CI 1.53, 4.85), ovary (HR 1.60; 95% CI 1.06, 2.42), prostate (HR 1.41; 95% CI 1.09, 1.82), kidney (HR 1.84; 95% CI 1.28, 2.64) and thyroid (HR 1.99; 95% CI 1.03, 3.86), as well as lymphoma (HR 1.39; 95% CI 1.04, 1.86). Diabetes was not statistically significantly associated with the risk of death from leukaemia and cancers of the bladder, cervix, oesophagus, stomach and lung. Conclusions/interpretation Diabetes was associated with a 26% increased risk of death from any cancer in Asians. The pattern of associations with specific cancers suggests the need for better control (prevention, detection, management) of the growing epidemic of diabetes (as well as obesity), in order to reduce cancer mortality.

To describe the application of the stepped wedge cluster randomized controlled trial (CRCT) design. Systematic review. We searched Medline, Embase, PsycINFO, HMIC, CINAHL, Cochrane Library, Web of Knowledge, and Current Controlled Trials Register for articles published up to January 2010. Stepped wedge CRCTs from all fields of research were included. Two authors independently reviewed and extracted data from the studies. Twenty-five studies were included in the review. Motivations for using the design included ethical, logistical, financial, social, and political acceptability and methodological reasons. Most studies were evaluating an intervention during routine implementation. For most of the included studies, there was also a belief or empirical evidence suggesting that the intervention would do more good than harm. There was variation in data analysis methods and insufficient quality of reporting. The stepped wedge CRCT design has been mainly used for evaluating interventions during routine implementation, particularly for interventions that have been shown to be effective in more controlled research settings, or where there is lack of evidence of effectiveness but there is a strong belief that they will do more good than harm. There is need for consistent data analysis and reporting.

Background Healthy lifestyles play an important role in the prevention of premature death, chronic diseases, productivity loss and other social and economic concerns. However, workplace interventions to address issues of fitness and nutrition which include work-related outcomes are complex and thus challenging to implement and appropriately measure the effectiveness of. This systematic review investigated the impact of workplace nutrition and physical activity interventions, which include components aimed at workplace’s physical environment and organizational structure, on employees’ productivity, work performance and workability. Methods A systematic review that included randomized controlled trials and or non-randomized controlled studies was conducted. Medline, EMBASE.com, Cochrane Library and Scopus were searched until September 2016. Productivity, absenteeism, presenteeism, work performance and workability were the primary outcomes of our interest, while sedentary behavior and changes in other health-related behaviors were considered as secondary outcomes. Two reviewers independently screened abstracts and full-texts for study eligibility, extracted the data and performed a quality assessment using the Cochrane Collaboration Risk-of-Bias Tool for randomized trials and the Risk-of-Bias in non-randomized studies of interventions. Findings were narratively synthesized. Results Thirty-nine randomized control trials and non-randomized controlled studies were included. Nearly 28% of the included studies were of high quality, while 56% were of medium quality. The studies covered a broad range of multi-level and environmental-level interventions. Fourteen workplace nutrition and physical activity intervention studies yielded statistically significant changes on absenteeism ( n  = 7), work performance ( n  = 2), workability ( n  = 3), productivity ( n  = 1) and on both workability and productivity ( n  = 1). Two studies showed effects on absenteeism only between subgroups. Conclusions The scientific evidence shows that it is possible to influence work-related outcomes, especially absenteeism, positively through health promotion efforts that include components aimed at the workplace’s physical work environment and organizational structure. In order to draw further conclusions regarding work-related outcomes in controlled high-quality studies, long-term follow-up using objective outcomes and/or quality assured questionnaires are required. Trial registration Registration number: PROSPERO CRD42017081837 .

We outline CONSORT-Equity 2017 reporting standards, an extension to the CONSORT (Consolidated Standards of Reporting Trials) statement that aims to improve the reporting of intervention effects in randomised trials where health equity is relevant. Health inequities are unfair differences in health that can be avoided by reasonable action. We defined a randomised trial where health equity is relevant as one that assesses effects on health equity by evaluating an intervention focused on people experiencing social disadvantage or by exploring the difference in the effect of the intervention between two groups (or as a gradient across more than two groups) experiencing different levels of social disadvantage, or both. We held a consensus meeting with diverse potential users from high, middle, and low income countries, including knowledge users such as patients and methodologists. We discussed evidence for each proposed extension item from empirical studies, reviews, key informant interviews, and an online survey, aiming to improve clarity of reporting without imposing undue burden on authors. The new guidance contains equity extensions to 16 items from CONSORT 2010 plus one new item on research ethics reporting, with examples of good practice and a brief explanation and elaboration for each. Widespread uptake of this guidance for the reporting of trials where health equity is relevant will make it easier for decision makers to find and use evidence from randomised trials to reduce unfair inequalities in health.

Randomized noncomparative trials (RNCTs) promise reduced accrual requirements vs randomized controlled comparative trials because RNCTs do not enroll a control group and instead compare outcomes to historical controls or prespecified estimates. We hypothesized that RNCTs often suffer from two methodological concerns: (1) lack of interpretability due to group-specific inferences in nonrandomly selected samples and (2) misinterpretation due to unlicensed between-group comparisons lacking prespecification. The purpose of this study was to characterize RNCTs and the incidence of these two methodological concerns. We queried PubMed and Web of Science on September 14, 2023, to conduct a meta-epidemiological analysis of published RNCTs in any field of medicine. Trial characteristics and the incidence of methodological concerns were manually recorded. We identified 70 RNCTs published from 2002 to 2023. RNCTs have been increasingly published over time (slope = 0.28, 95% CI 0.17–0.39, P < .001). Sixty trials (60/70, 86%) had a lack of interpretability for the primary endpoint due to group-specific inferences. Unlicensed between-group comparisons were present in 36 trials (36/70, 51%), including in the primary conclusion of 31 trials (31/70, 44%), and were accompanied by significance testing in 20 trials (20/70, 29%). Only five (5/70, 7%) trials were found to have neither of these flaws. Although RNCTs are increasingly published over time, the primary analysis of nearly all published RNCTs in the medical literature was unsupported by their fundamental underlying methodological assumptions. RNCTs promise group-specific inference, which they are unable to deliver, and undermine the primary advantage of randomization, which is comparative inference. The ongoing use of the RNCT design in lieu of a traditional randomized controlled comparative trial should therefore be reconsidered.

Background Multiple primary outcomes may be specified in randomised controlled trials (RCTs). When analysing multiple outcomes it’s important to control the family wise error rate (FWER). A popular approach to do this is to adjust the p -values corresponding to each statistical test used to investigate the intervention effects by using the Bonferroni correction. It’s also important to consider the power of the trial to detect true intervention effects. In the context of multiple outcomes, depending on the clinical objective, the power can be defined as: ‘disjunctive power’ , the probability of detecting at least one true intervention effect across all the outcomes or ‘ marginal power’ the probability of finding a true intervention effect on a nominated outcome. We provide practical recommendations on which method may be used to adjust for multiple comparisons in the sample size calculation and the analysis of RCTs with multiple primary outcomes. We also discuss the implications on the sample size for obtaining 90% disjunctive power and 90% marginal power. Methods We use simulation studies to investigate the disjunctive power, marginal power and FWER obtained after applying Bonferroni, Holm, Hochberg, Dubey/Armitage-Parmar and Stepdown-minP adjustment methods. Different simulation scenarios were constructed by varying the number of outcomes, degree of correlation between the outcomes, intervention effect sizes and proportion of missing data. Results The Bonferroni and Holm methods provide the same disjunctive power. The Hochberg and Hommel methods provide power gains for the analysis, albeit small, in comparison to the Bonferroni method. The Stepdown-minP procedure performs well for complete data. However, it removes participants with missing values prior to the analysis resulting in a loss of power when there are missing data. The sample size requirement to achieve the desired disjunctive power may be smaller than that required to achieve the desired marginal power. The choice between whether to specify a disjunctive or marginal power should depend on the clincial objective.

Cluster randomized trials (CRTs) are frequently used to evaluate interventions in low- and middle-income countries (LMICs). Robust execution and transparent reporting of randomization procedures are essential for successful implementation and accurate interpretation of CRTs. Our objectives were to review the quality of reporting and implementation of randomization procedures in a sample of parallel-arm CRTs conducted in LMICs. We selected a random sample of 300 primary reports of parallel-arm CRTs from a database of 800 CRTs conducted in LMICs between 2017 and 2022. Data were extracted by two reviewers per trial and summarized using descriptive statistics. Among 300 trials, 192 (64%) reported the method of sequence generation, 213 (71%) reported the type of randomization procedure used, 146 (49%) reported who generated the sequence, 136 (45%) reported whether randomization was implemented by an independent person, and 75 (25%) reported a method of allocation concealment. Among those reporting the methods used, suboptimal randomization procedures were common: 28% did not use a computer, 21% did not use restricted randomization, 58% did not use a statistician to generate the sequence, in 53% the person was not independent from the trial, and 80% did not use central randomization. Public randomization ceremonies were used in 10% of trials as an alternative method of allocation concealment and to reassure participants of fair allocation procedures. The conduct and reporting of randomization procedures of CRTs in LMICs is suboptimal. Dissemination of guidance to promote robust implementation of randomization in LMICs is required, and future research on the implementation of public randomization ceremonies is warranted. Cluster randomized trials (CRTs) are trials where entire groups, rather than individuals, are randomly assigned to different treatments (eg, intervention or usual care). This randomization process can be challenging in CRTs; clear reporting and proper execution are important to ensure fairness and accurate results. In this study, we reviewed how well randomization procedures were reported and carried out in 300 CRTs, selected from a larger database of 800 CRTs, conducted in low- and middle-income countries (LMICs), and published between 2017 and 2022. We found that reporting on key aspects of randomization was often incomplete: 64% reported how they created the random allocation sequence, 71% reported the type of randomization method used, 49% reported who generated the sequence, 45% reported whether a person independent from the trial handled the randomization, and 25% reported how they kept group assignments hidden until the intervention was ready to begin. Even when trials did reported these methods, many did not follow best practices: 28% did not use a computer, 21% did not apply techniques to ensure balanced treatment arms, 58% did not involve a statistician to generate the sequence, 53% had someone involved in the trial handle randomization (as opposed to an independent person), and 80% did not use central randomization to assign groups, where a third party reveals treatment assignment to groups. Interestingly, 10% of trials used public randomization ceremonies (events where group assignments are revealed in a public setting) to keep group assignments hidden until revealment and to reassure participants that the process was fair. Overall, we found that randomization procedures in CRTs were often not well reported or carried out optimally. It is important for researchers to follow established guidelines to ensure randomization is done properly in CRTs in LMICs. More research is also needed to understand how public randomization ceremonies are used in practice. [Display omitted] •Robust randomization methods are essential for cluster randomized trials (CRTs).•Improved adherence to reporting and best practices for randomization in CRTs is needed.•Public randomization ceremonies may help with implementation challenges.•Further research on the conduct of public randomization ceremonies is warranted.