Cost-effectiveness of Microsoft Academic Graph with machine learning for automated study identification in a living map of coronavirus disease 2019 (COVID-19) research.

Background: Identifying new, eligible studies for integration into living systematic reviews and maps usually relies on conventional Boolean updating searches of multiple databases and manual processing of the updated results. Automated searches of one, comprehensive, continuously updated source, with adjunctive machine learning, could enable more efficient searching, selection and prioritisation workflows for updating (living) reviews and maps, though research is needed to establish this. Microsoft Academic Graph (MAG) is a potentially comprehensive single source which also contains metadata that can be used in machine learning to help efficiently identify eligible studies.

Methods used to conceptualize dimensions of health equity impacts of public health interventions in systematic reviews.

Objectives: Our aims were to, first, identify and summarize the use of methods, frameworks, and tools as a conceptual basis for investigating dimensions of equity impacts of public health interventions in systematic reviews including an equity focus. These include PROGRESS-Plus, which identifies key sociodemographic characteristics that determine health outcomes. Second, we aimed to document challenges and opportunities encountered in the application of such methods, as reported in systematic reviews.

GRADE guidance 23: considering cost-effectiveness evidence in moving from evidence to health-related recommendations.

Background: This is the 23rd in a series of articles describing the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to grading the certainty of evidence and strength of recommendations for systematic reviews, health technology assessments, and clinical guideline development.

Objectives: We outline how resource utilization and cost-effectiveness analyses are integrated into health-related recommendations, using the GRADE Evidence to Decision (EtD) frameworks.

Study Design And Setting: Through iterative discussions and refinement, in-person, and online meetings, and through e-mail communication, we developed draft guidance to incorporate economic evidence in the formulation of health-related recommendations.

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.

Incentives for climate mitigation in the land use sector-the effects of payment for environmental services on environmental and socioeconomic outcomes in low- and middle-income countries: A mixed-methods systematic review.

Unsustainable practices in the land use sector contribute to climate change through the release of greenhouse gases. Payment for environmental services (PESs) provide economic incentives to reduce the negative environmental impacts of land use and are a popular approach to mitigate climate change in low- and middle-income countries. Some PES programmes also aim to improve socioeconomic outcomes and reduce poverty.

Altering the availability or proximity of food, alcohol, and tobacco products to change their selection and consumption.

Background: Overconsumption of food, alcohol, and tobacco products increases the risk of non-communicable diseases. Interventions to change characteristics of physical micro-environments where people may select or consume these products - including shops, restaurants, workplaces, and schools - are of considerable public health policy and research interest. This review addresses two types of intervention within such environments: altering the availability (the range and/or amount of options) of these products, or their proximity (the distance at which they are positioned) to potential consumers.

Altering the availability or proximity of food, alcohol, and tobacco products to change their selection and consumption.

Background: Overconsumption of food, alcohol, and tobacco products increases the risk of non-communicable diseases. Interventions to change characteristics of physical micro-environments where people may select or consume these products - including shops, restaurants, workplaces, and schools - are of considerable public health policy and research interest. This review addresses two types of intervention within such environments: altering the availability (the range and/or amount of options) of these products, or their proximity (the distance at which they are positioned) to potential consumers.

The crime, mental health, and economic impacts of prearrest diversion of people with mental health problems: A systematic review.

Background: Prearrest diversion strategies are being adopted across the Western world, enabling the police to identify and divert people suspected of having mental disorder towards health and community services rather than the criminal justice system.

Aims: To quantify longer-term criminal justice and mental health outcomes after prearrest diversion of people with suspected mental disorder and consider economic correlates.

Methods: A systematic review of published literature on longer term outcomes after prearrest diversion.

Still moving toward automation of the systematic review process: a summary of discussions at the third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR).

The third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 17-18 October 2017 in London, England. ICASR is an interdisciplinary group whose goal is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. The group seeks to facilitate the development and widespread acceptance of automated techniques for systematic reviews.

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.

Living systematic review: 1. Introduction-the why, what, when, and how.

Systematic reviews are difficult to keep up to date, but failure to do so leads to a decay in review currency, accuracy, and utility. We are developing a novel approach to systematic review updating termed "Living systematic review" (LSR): systematic reviews that are continually updated, incorporating relevant new evidence as it becomes available. LSRs may be particularly important in fields where research evidence is emerging rapidly, current evidence is uncertain, and new research may change policy or practice decisions.

Quasi-experimental study designs series-paper 1: introduction: two historical lineages.

Objectives: The objective of this study was to contrast the historical development of experiments and quasi-experiments and provide the motivation for a journal series on quasi-experimental designs in health research.

Study Design And Setting: A short historical narrative, with concrete examples, and arguments based on an understanding of the practice of health research and evidence synthesis.

Results: Health research has played a key role in developing today's gold standard for causal inference-the randomized controlled multiply blinded trial.

Quasi-experimental study designs series-paper 8: identifying quasi-experimental studies to inform systematic reviews.

Objective: This article reviews the available evidence and guidance on methods to identify reports of quasi-experimental (QE) studies to inform systematic reviews of health care, public health, international development, education, crime and justice, and social welfare.

Study Design And Setting: Research, guidance, and examples of search strategies were identified by searching a range of databases, key guidance documents, selected reviews, conference proceedings, and personal communication. Current practice and research evidence were summarized.

Quasi-experimental study designs series-paper 9: collecting data from quasi-experimental studies.

Objective: To identify variables that must be coded when synthesizing primary studies that use quasi-experimental designs.

Study Design And Setting: All quasi-experimental (QE) designs.

Results: When designing a systematic review of QE studies, potential sources of heterogeneity-both theory-based and methodological-must be identified.

Quasi-experimental study designs series-paper 4: uses and value.

Quasi-experimental studies are increasingly used to establish causal relationships in epidemiology and health systems research. Quasi-experimental studies offer important opportunities to increase and improve evidence on causal effects: (1) they can generate causal evidence when randomized controlled trials are impossible; (2) they typically generate causal evidence with a high degree of external validity; (3) they avoid the threats to internal validity that arise when participants in nonblinded experiments change their behavior in response to the experimental assignment to either intervention or control arm (such as compensatory rivalry or resentful demoralization); (4) they are often well suited to generate causal evidence on long-term health outcomes of an intervention, as well as nonhealth outcomes such as economic and social consequences; and (5) they can often generate evidence faster and at lower cost than experiments and other intervention studies.

Multi-arm Cost-Effectiveness Analysis (CEA) comparing different durations of adjuvant trastuzumab in early breast cancer, from the English NHS payer perspective.

Background: Trastuzumab improves survival in HER2+ breast cancer patients, with some evidence of adverse cardiac side effects. Current recommendations are to give adjuvant trastuzumab for one year or until recurrence, although trastuzumab treatment for only 9 or 10 weeks has shown similar survival rates to 12-month treatment. We present here a multi-arm joint analysis examining the relative cost-effectiveness of different durations of adjuvant trastuzumab.

Documenting research with transgender and gender diverse people: protocol for an evidence map and thematic analysis.

Background: There is limited information about how transgender, gender diverse, and Two-Spirit (trans) people have been represented and studied by researchers. The objectives of this study are to (1) map and describe trans research in the social sciences, sciences, humanities, health, education, and business, (2) identify evidence gaps and opportunities for more responsible research with trans people, (3) assess the use of text mining for study identification, and (4) increase access to trans research for key stakeholders through the creation of a web-based evidence map.

Methods: Study design was informed by community consultations and pilot searches.

What do we know about the effects of exposure to 'Low alcohol' and equivalent product labelling on the amounts of alcohol, food and tobacco people select and consume? A systematic review.

Background: Explicit labelling of lower strength alcohol products could reduce alcohol consumption by attracting more people to buy and drink such products instead of higher strength ones. Alternatively, it may lead to more consumption due to a 'self-licensing' mechanism. Equivalent labelling of food or tobacco (for example "Low fat" or "Low tar") could influence consumption of those products by similar mechanisms.

Use of cost-effectiveness analysis to compare the efficiency of study identification methods in systematic reviews.

Background: Meta-research studies investigating methods, systems, and processes designed to improve the efficiency of systematic review workflows can contribute to building an evidence base that can help to increase value and reduce waste in research. This study demonstrates the use of an economic evaluation framework to compare the costs and effects of four variant approaches to identifying eligible studies for consideration in systematic reviews.

Methods: A cost-effectiveness analysis was conducted using a basic decision-analytic model, to compare the relative efficiency of 'safety first', 'double screening', 'single screening' and 'single screening with text mining' approaches in the title-abstract screening stage of a 'case study' systematic review about undergraduate medical education in UK general practice settings.

Immediate effects of alcohol marketing communications and media portrayals on consumption and cognition: a systematic review and meta-analysis of experimental studies.

Background: Restricting marketing of alcoholic products is purported to be a cost-effective intervention to reduce alcohol consumption. The strength of evidence supporting this claim is contested. This systematic review aimed to assess immediate effects of exposure to alcohol marketing on alcoholic beverage consumption and related cognitions.