Rapid review methods series: Guidance on the use of supportive software.

This paper is part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group. Rapid reviews (RRs) use modified systematic review methods to accelerate the review process while maintaining systematic, transparent and reproducible methods. This paper guides how to use supportive software for RRs.

Machine learning reduced workload for the Cochrane COVID-19 Study Register: development and evaluation of the Cochrane COVID-19 Study Classifier.

Background: This study developed, calibrated and evaluated a machine learning (ML) classifier designed to reduce study identification workload in maintaining the Cochrane COVID-19 Study Register (CCSR), a continuously updated register of COVID-19 research studies.

Methods: A ML classifier for retrieving COVID-19 research studies (the 'Cochrane COVID-19 Study Classifier') was developed using a data set of title-abstract records 'included' in, or 'excluded' from, the CCSR up to 18th October 2020, manually labelled by information and data curation specialists or the Cochrane Crowd. The classifier was then calibrated using a second data set of similar records 'included' in, or 'excluded' from, the CCSR between October 19 and December 2, 2020, aiming for 99% recall.

An evaluation of Cochrane Crowd found that crowdsourcing produced accurate results in identifying randomized trials.

Background And Objectives: Filtering the deluge of new research to facilitate evidence synthesis has proven to be unmanageable using current paradigms of search and retrieval. Crowdsourcing, a way of harnessing the collective effort of a "crowd" of people, has the potential to support evidence synthesis by addressing this information overload created by the exponential growth in primary research outputs. Cochrane Crowd, Cochrane's citizen science platform, offers a range of tasks aimed at identifying studies related to health care.

Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews.

Objectives: This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews.

Methods: 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.

Cochrane's Linked Data Project: How it Can Advance our Understanding of Surrogate Endpoints.

Cochrane has developed a linked data infrastructure to make the evidence and data from its rich repositories more discoverable to facilitate evidence-based health decision-making. These annotated resources can enhance the study and understanding of biomarkers and surrogate endpoints.

Facilitating Web-Based Collaboration in Evidence Synthesis (TaskExchange): Development and Analysis.

Background: The conduct and publication of scientific research are increasingly open and collaborative. There is growing interest in Web-based platforms that can effectively enable global, multidisciplinary scientific teams and foster networks of scientists in areas of shared research interest. Designed to facilitate Web-based collaboration in research evidence synthesis, TaskExchange highlights the potential of these kinds of platforms.

A taxonomy has been developed for outcomes in medical research to help improve knowledge discovery.

Objectives: There is increasing recognition that insufficient attention has been paid to the choice of outcomes measured in clinical trials. The lack of a standardized outcome classification system results in inconsistencies due to ambiguity and variation in how outcomes are described across different studies. Being able to classify by outcome would increase efficiency in searching sources such as clinical trial registries, patient registries, the Cochrane Database of Systematic Reviews, and the Core Outcome Measures in Effectiveness Trials (COMET) database of core outcome sets (COS), thus aiding knowledge discovery.

Living systematic reviews: 2. Combining human and machine effort.

New approaches to evidence synthesis, which use human effort and machine automation in mutually reinforcing ways, can enhance the feasibility and sustainability of living systematic reviews. Human effort is a scarce and valuable resource, required when automation is impossible or undesirable, and includes contributions from online communities ("crowds") as well as more conventional contributions from review authors and information specialists. Automation can assist with some systematic review tasks, including searching, eligibility assessment, identification and retrieval of full-text reports, extraction of data, and risk of bias assessment.

When and how to update systematic reviews: consensus and checklist.

Updating of systematic reviews is generally more efficient than starting all over again when new evidence emerges, but to date there has been no clear guidance on how to do this. This guidance helps authors of systematic reviews, commissioners, and editors decide when to update a systematic review, and then how to go about updating the review.

Living systematic reviews: an emerging opportunity to narrow the evidence-practice gap.

The current difficulties in keeping systematic reviews up to date leads to considerable inaccuracy, hampering the translation of knowledge into action. Incremental advances in conventional review updating are unlikely to lead to substantial improvements in review currency. A new approach is needed.

Long-acting beta2-agonists for chronic obstructive pulmonary disease.

Background: Chronic obstructive pulmonary disease (COPD) is a respiratory disease that causes progressive symptoms of breathlessness, cough and mucus build-up. It is the fourth or fifth most common cause of death worldwide and is associated with significant healthcare costs.Inhaled long-acting beta2-agonists (LABAs) are widely prescribed to manage the symptoms of COPD when short-acting agents alone are no longer sufficient.

Can decision making in general surgery be based on evidence? An empirical study of Cochrane Reviews.

Background: This empirical study analyzes the current status of Cochrane Reviews (CRs) and their strength of recommendation for evidence-based decision making in the field of general surgery.

Methods: Systematic literature search of the Cochrane Database of Systematic Reviews and the Cochrane Collaboration's homepage to identify available CRs on surgical topics. Quantitative and qualitative characteristics, utilization, and formulated treatment recommendations were evaluated by 2 independent reviewers.