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Abstract Introduction Network meta-analyses (NMAs) have gained popularity and grown in number due to their ability to provide estimates of the comparative effectiveness of multiple treatments for the same condition. The aim of this study is to conduct a methodological review to compile a preliminary list of concepts related to bias in NMAs. Methods and analysis We included papers that present items related to bias, reporting or methodological quality, papers assessing the quality of NMAs, or method papers. We searched MEDLINE, the Cochrane Library and unpublished literature (up to July 2020). We extracted items related to bias in NMAs. An item was excluded if it related to general systematic review quality or bias and was included in currently available tools such as ROBIS or AMSTAR 2. We reworded items, typically structured as questions, into concepts (i.e. general notions). Results One hundred eighty-one articles were assessed in full text and 58 were included. Of these articles, 12 were tools, checklists or journal standards; 13 were guidance documents for NMAs; 27 were studies related to bias or NMA methods; and 6 were papers assessing the quality of NMAs. These studies yielded 99 items of which the majority related to general systematic review quality and biases and were therefore excluded. The 22 items we included were reworded into concepts specific to bias in NMAs. Conclusions A list of 22 concepts was included. This list is not intended to be used to assess biases in NMAs, but to inform the development of items to be included in our tool.

Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search PubMed, ACL Anthology, arXiv, OpenAlex via EPPI-Reviewer, and the dblp computer science bibliography. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This living review update includes publications up to December 2022 and OpenAlex content up to March 2023. Results: 76 publications are included in this review. Of these, 64 (84%) of the publications addressed extraction of data from abstracts, while 19 (25%) used full texts. A total of 71 (93%) publications developed classifiers for randomised controlled trials. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. Data are available from 25 (33%), and code from 30 (39%) publications. Six (8%) implemented publicly available tools Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of literature review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. Between review updates, trends for sharing data and code increased strongly: in the base-review, data and code were available for 13 and 19% respectively, these numbers increased to 78 and 87% within the 23 new publications. Compared with the base-review, we observed another research trend, away from straightforward data extraction and towards additionally extracting relations between entities or automatic text summarisation. With this living review we aim to review the literature continually.

Abstract Background Evidence from previous studies is often used relatively informally in the design of clinical trials: for example, a systematic review to indicate whether a gap in the current evidence base justifies a new trial. External evidence can be used more formally in both trial design and analysis, by explicitly incorporating a synthesis of it in a Bayesian framework. However, it is unclear how common this is in practice or the extent to which it is considered controversial. In this qualitative study, we explored attitudes towards, and experiences of, trialists in incorporating synthesised external evidence through the Bayesian design or analysis of a trial. Methods Semi-structured interviews were conducted with 16 trialists: 13 statisticians and three clinicians. Participants were recruited across several universities and trials units in the United Kingdom using snowball and purposeful sampling. Data were analysed using thematic analysis and techniques of constant comparison. Results Trialists used existing evidence in many ways in trial design, for example, to justify a gap in the evidence base and inform parameters in sample size calculations. However, no one in our sample reported using such evidence in a Bayesian framework. Participants tended to equate Bayesian analysis with the incorporation of prior information on the intervention effect and were less aware of the potential to incorporate data on other parameters. When introduced to the concepts, many trialists felt they could be making more use of existing data to inform the design and analysis of a trial in particular scenarios. For example, some felt existing data could be used more formally to inform background adverse event rates, rather than relying on clinical opinion as to whether there are potential safety concerns. However, several barriers to implementing these methods in practice were identified, including concerns about the relevance of external data, acceptability of Bayesian methods, lack of confidence in Bayesian methods and software, and practical issues, such as difficulties accessing relevant data. Conclusions Despite trialists recognising that more formal use of external evidence could be advantageous over current approaches in some areas and useful as sensitivity analyses, there are still barriers to such use in practice.

Abstract Background Selective outcome reporting and publication bias threaten the validity of systematic reviews and meta-analyses and can affect clinical decision-making. A rigorous method to evaluate the impact of this bias on the results of network meta-analyses of interventions is lacking. We present a tool to assess the Risk Of Bias due to Missing Evidence in Network meta-analysis (ROB-MEN). Methods ROB-MEN first evaluates the risk of bias due to missing evidence for each of the possible pairwise comparison that can be made between the interventions in the network. This step considers possible bias due to the presence of studies with unavailable results (within-study assessment of bias) and the potential for unpublished studies (across-study assessment of bias). The second step combines the judgements about the risk of bias due to missing evidence in pairwise comparisons with (i) the contribution of direct comparisons to the network meta-analysis estimates, (ii) possible small-study effects evaluated by network meta-regression, and (iii) any bias from unobserved comparisons. Then, a level of “low risk”, “some concerns”, or “high risk” for the bias due to missing evidence is assigned to each estimate, which is our tool’s final output. Results We describe the methodology of ROB-MEN step-by-step using an illustrative example from a published NMA of non-diagnostic modalities for the detection of coronary artery disease in patients with low risk acute coronary syndrome. We also report a full application of the tool on a larger and more complex published network of 18 drugs from head-to-head studies for the acute treatment of adults with major depressive disorder. Conclusions ROB-MEN is the first tool for evaluating the risk of bias due to missing evidence in network meta-analysis and applies to networks of all sizes and geometry. The use of ROB-MEN is facilitated by an R Shiny web application that produces the Pairwise Comparisons and ROB-MEN Table and is incorporated in the reporting bias domain of the CINeMA framework and software.

Abstract Background Network meta-analysis (NMA) has attracted growing interest in evidence-based medicine. Consistency between different sources of evidence is fundamental to the reliability of the NMA results. The purpose of the present study was to estimate the prevalence of evidence of inconsistency and describe its association with different NMA characteristics. Methods We updated our collection of NMAs with articles published up to July 2018. We included networks with randomised clinical trials, at least four treatment nodes, at least one closed loop, a dichotomous primary outcome, and available arm-level data. We assessed consistency using the design-by-treatment interaction (DBT) model and testing all the inconsistency parameters globally through the Wald-type chi-squared test statistic. We estimated the prevalence of evidence of inconsistency and its association with different network characteristics (e.g., number of studies, interventions, intervention comparisons, loops). We evaluated the influence of the network characteristics on the DBT p-value via a multivariable regression analysis and the estimated Pearson correlation coefficients. We also evaluated heterogeneity in NMA (consistency) and DBT (inconsistency) random-effects models. Results We included 201 published NMAs. The p-value of the design-by-treatment interaction (DBT) model was lower than 0.05 in 14% of the networks and lower than 0.10 in 20% of the networks. Networks including many studies and comparing few interventions were more likely to have small DBT p-values (less than 0.10), which is probably because they yielded more precise estimates and power to detect differences between designs was higher. In the presence of inconsistency (DBT p-value lower than 0.10), the consistency model displayed higher heterogeneity than the DBT model. Conclusions Our findings show that inconsistency was more frequent than what would be expected by chance, suggesting that researchers should devote more resources to exploring how to mitigate inconsistency. The results of this study highlight the need to develop strategies to detect inconsistency (because of the relatively high prevalence of evidence of inconsistency in published networks), and particularly in cases where the existing tests have low power.

Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to "map" the outcomes onto a single scale. This is particularly challenging when trials report aggregate rather than individual data. We are motivated by a meta-analysis of interventions to prevent obesity in children. Trials report aggregate measurements of body mass index (BMI) either expressed as raw values or standardized for age and sex. We develop three methods for mapping between aggregate BMI data using known or estimated relationships between measurements on different scales at the individual level. The first is an analytical method based on the mathematical definitions of z-scores and percentiles. The other two approaches involve sampling individual participant data on which to perform the conversions. One method is a straightforward sampling routine, while the other involves optimization with respect to the reported outcomes. In contrast to the analytical approach, these methods also have wider applicability for mapping between any pair of measurement scales with known or estimable individual-level relationships. We verify and contrast our methods using simulation studies and trials from our data set which report outcomes on multiple scales. We find that all methods recreate mean values with reasonable accuracy, but for standard deviations, optimization outperforms the other methods. However, the optimization method is more likely to underestimate standard deviations and is vulnerable to non-convergence.

Abstract Background Investigations of transparency, reproducibility and replicability in science have been directed largely at individual studies. It is just as critical to explore these issues in syntheses of studies, such as systematic reviews, given their influence on decision-making and future research. We aim to explore various aspects relating to the transparency, reproducibility and replicability of several components of systematic reviews with meta-analysis of the effects of health, social, behavioural and educational interventions. Methods The REPRISE (REProducibility and Replicability In Syntheses of Evidence) project consists of four studies. We will evaluate the completeness of reporting and sharing of review data, analytic code and other materials in a random sample of 300 systematic reviews of interventions published in 2020 (Study 1). We will survey authors of systematic reviews to explore their views on sharing review data, analytic code and other materials and their understanding of and opinions about replication of systematic reviews (Study 2). We will then evaluate the extent of variation in results when we (a) independently reproduce meta-analyses using the same computational steps and analytic code (if available) as used in the original review (Study 3), and (b) crowdsource teams of systematic reviewers to independently replicate a subset of methods (searches for studies, selection of studies for inclusion, collection of outcome data, and synthesis of results) in a sample of the original reviews; 30 reviews will be replicated by 1 team each and 2 reviews will be replicated by 15 teams (Study 4). Discussion The REPRISE project takes a systematic approach to determine how reliable systematic reviews of interventions are. We anticipate that results of the REPRISE project will inform strategies to improve the conduct and reporting of future systematic reviews.

There is widespread concern over the potential impact of the COVID-19 pandemic on suicide and self-harm globally, particularly in low- and middle-income countries (LMIC) where the burden of these behaviours is greatest. We synthesised the evidence from the published literature on the impact of the pandemic on suicide and self-harm in LMIC. This review is nested within a living systematic review (PROSPERO ID CRD42020183326) that continuously identifies published evidence (all languages) through a comprehensive automated search of multiple databases (PubMed; Scopus; medRxiv, PsyArXiv; SocArXiv; bioRxiv; the WHO COVID-19 database; and the COVID-19 Open Research Dataset by Semantic Scholar (up to 11/2020), including data from Microsoft Academic, Elsevier, arXiv and PubMed Central.) All articles identified by the 4th August 2021 were screened. Papers reporting on data from a LMIC and presenting evidence on the impact of the pandemic on suicide or self-harm were included. Methodological quality was assessed using an appropriate tool, and a narrative synthesis presented. A total of 22 studies from LMIC were identified representing data from 12 countries. There was an absence of data from Africa, the Pacific, and the Caribbean. The reviewed studies mostly report on the early months of COVID-19 and were generally methodologically poor. Few studies directly assessed the impact of the pandemic. The most robust evidence, from time-series studies, indicate either a reduction or no change in suicide and self-harm behaviour. As LMIC continue to experience repeated waves of the virus and increased associated mortality, against a backdrop of vaccine inaccessibility and limited welfare support, continued efforts are needed to track the indirect impact of the pandemic on suicide and self-harm in these countries.

Abstract Objectives Primary objective: To estimate the effect of corticosteroids compared with usual care or placebo on mortality up to 28 days after randomization. Secondary objectives: To examine whether the effect of corticosteroids compared with usual care or placebo on mortality up to 28 days after randomization varies between subgroups related to treatment characteristics, disease severity at the time of randomization, patient characteristics, or risk of bias. To examine the effect of corticosteroids compared with usual care or placebo on serious adverse events. Study design Prospective meta-analysis of randomized controlled trials. Both placebo-controlled and open-label trials are eligible. Participants Hospitalised, critically ill patients with suspected or confirmed COVID-19. Intervention and comparator Intervention groups will have received therapeutic doses of a steroid (dexamethasone, hydrocortisone or methylprednisolone) with IV or oral administration immediately after randomization. The comparator groups will have received standard of care or usual care or placebo. Main outcome All-cause mortality up to 28 days after randomization. Search methods Systematic searching of clinicaltrials.gov , EudraCT, the WHO ISRCTN registry, and the Chinese clinical trials registry. Additionally, research and WHO networks will be asked for relevant trials. Risk of bias assessments These will be based on the Cochrane RoB 2 tool, and will use structured information provided by the trial investigators on a form designed for this prospective meta-analysis. Summary of findings We will use GRADE to assess the certainty of the evidence. Statistical analyses Trial investigators will provide data on the numbers of participants who did and did not experience each outcome according to intervention group, overall and in specified subgroups. We will conduct fixed-effect (primary analysis) and random-effects (Paule-Mandel estimate of heterogeneity and Hartung-Knapp adjustment) meta-analyses. We will quantify inconsistency in effects between trials using I2 statistics. Evidence for subgroup effects will be quantified by ratios of odds ratios comparing effects in the subgroups, and corresponding interaction p-values. Comparisons between subgroups defined by trial characteristics will be made using random-effects meta-regression. Comparisons between subgroups defined by patient characteristics will be made by estimating trial-specific ratios of odds ratios comparing intervention effects between subgroups then combining these using random-effects meta-analysis. Steroid interventions will be classified as high or low dose according to whether the dose is greater or less than or equal to 400 mg hydrocortisone per day or equivalent. We will use network meta-analysis methods to make comparisons between the effects of high and low dose steroid interventions (because one trial randomized participants to both low and high dose steroid arms). PROSPERO registration number CRD42020197242 Full protocol The full protocol for this prospective meta-analysis is attached as an additional file, accessible from the Trials website (Additional file 1). To expedite dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol for the systematic review.

Abstract Background Evidence from studies on prostate cancer progression have identified vitamin D to be a potentially important nutrient. However, the World Cancer Research Fund and American Institute for Cancer Research have reported the quality of this evidence to be limited and warrant further investigation. We plan to use the recently developed WCRF International/University of Bristol mechanistic systematic review framework to determine whether the observed association between vitamin D and prostate cancer exists through a plausible biological pathway. Methods This protocol sets out how we will perform a systematic review of the literature in human and animal studies. We will search the electronic databases MEDLINE, EMBASE, PubMed, and BIOSIS Citation Index without restrictions on year of publication or language. We will extract data from observational and experimental studies examining two inter-linked pathways in the relationship between vitamin D and prostate cancer progression: (1) vitamin D and testosterone, and (2) testosterone and prostate cancer progression. We focus on testosterone as its actions form a potentially novel intermediate mechanism that was identified via our online literature mining tools. The outcomes of interest include incidence or prevalence of prostate cancer, measures of prostate cancer progression (including biochemical recurrence, local, or distal metastases), and prostate cancer-specific mortality. We will assess study quality and the level of certainty of the evidence. We will analyse data where possible, using meta-analysis with forest plots or albatross plots; otherwise, a narrative synthesis will be performed. Discussion To our knowledge, this will be the first systematic synthesis of the evidence underpinning the vitamin D-testosterone-prostate cancer mechanistic pathway. The results of the review may inform future research, intervention trials, and public health messages.

Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search MEDLINE, Institute of Electrical and Electronics Engineers (IEEE), arXiv, and the dblp computer science bibliography databases. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This iteration of the living review includes publications up to a cut-off date of 22 April 2020. Results: In total, 53 publications are included in this version of our review. Of these, 41 (77%) of the publications addressed extraction of data from abstracts, while 14 (26%) used full texts. A total of 48 (90%) publications developed and evaluated classifiers that used randomised controlled trials as the main target texts. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. A description of their datasets was provided by 49 publications (94%), but only seven (13%) made the data publicly available. Code was made available by 10 (19%) publications, and five (9%) implemented publicly available tools. Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of systematic review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. The lack of publicly available gold-standard data for evaluation, and lack of application thereof, makes it difficult to draw conclusions on which is the best-performing system for each data extraction target. With this living review we aim to review the literature continually.

Background: Researchers in evidence-based medicine cannot keep up with the amounts of both old and newly published primary research articles. Support for the early stages of the systematic review process – searching and screening studies for eligibility – is necessary because it is currently impossible to search for relevant research with precision. Better automated data extraction may not only facilitate the stage of review traditionally labelled ‘data extraction’, but also change earlier phases of the review process by making it possible to identify relevant research. Exponential improvements in computational processing speed and data storage are fostering the development of data mining models and algorithms. This, in combination with quicker pathways to publication, led to a large landscape of tools and methods for data mining and extraction. Objective: To review published methods and tools for data extraction to (semi)automate the systematic reviewing process. Methods: We propose to conduct a living review. With this methodology we aim to do constant evidence surveillance, bi-monthly search updates, as well as review updates every 6 months if new evidence permits it. In a cross-sectional analysis we will extract methodological characteristics and assess the quality of reporting in our included papers. Conclusions: We aim to increase transparency in the reporting and assessment of automation technologies to the benefit of data scientists, systematic reviewers and funders of health research. This living review will help to reduce duplicate efforts by data scientists who develop data mining methods. It will also serve to inform systematic reviewers about possibilities to support their data extraction.

Abstract Network meta‐analysis (NMA) compares several interventions that are linked in a network of comparative studies and estimates the relative treatment effects between all treatments, using both direct and indirect evidence. NMA is increasingly used for decision making in health care, however, a user‐friendly system to evaluate the confidence that can be placed in the results of NMA is currently lacking. This paper is a tutorial describing the Confidence In Network Meta‐Analysis (CINeMA) web application, which is based on the framework developed by Salanti et al (2014, PLOS One, 9, e99682) and refined by Nikolakopoulou et al (2019, bioRxiv). Six domains that affect the level of confidence in the NMA results are considered: (a) within‐study bias, (b) reporting bias, (c) indirectness, (d) imprecision, (e) heterogeneity, and (f) incoherence. CINeMA is freely available and open‐source and no login is required. In the configuration step users upload their data, produce network plots and define the analysis and effect measure. The dataset should include assessments of study‐level risk of bias and judgments on indirectness. CINeMA calls the netmeta routine in R to estimate relative effects and heterogeneity. Users are then guided through a systematic evaluation of the six domains. In this way reviewers assess the level of concerns for each relative treatment effect from NMA as giving rise to “no concerns,” “some concerns,” or “major concerns” in each of the six domains, which are graphically summarized on the report page for all effect estimates. Finally, judgments across the domains are summarized into a single confidence rating (“high,” “moderate,” “low,” or “very low”). In conclusion, the user‐friendly web‐based CINeMA platform provides a transparent framework to evaluate evidence from systematic reviews with multiple interventions.

Abstract Background We estimated the effectiveness of serial B-type natriuretic peptide (BNP) blood testing to guide up-titration of medication compared with symptom-guided up-titration of medication in patients with heart failure (HF). Methods Systematic review and meta-analysis of randomised controlled trials (RCTs). We searched: MEDLINE (Ovid) 1950 to 9/06/2016; Embase (Ovid), 1980 to 2016 week 23; the Cochrane Library; ISI Web of Science (Citations Index and Conference Proceedings). The primary outcome was all-cause mortality; secondary outcomes were death related to HF, cardiovascular death, all-cause hospital admission, hospital admission for HF, adverse events, and quality of life. IPD were sought from all RCTs identified. Random-effects meta-analyses (two-stage) were used to estimate hazard ratios (HR) and confidence intervals (CIs) across RCTs, including HR estimates from published reports of studies that did not provide IPD. We estimated treatment-by-covariate interactions for age, gender, New York Heart Association (NYHA) class, HF type; diabetes status and baseline BNP subgroups. Dichotomous outcomes were analysed using random-effects odds ratio (OR) with 95% CI. Results We identified 14 eligible RCTs, five providing IPD. BNP-guided therapy reduced the hazard of hospital admission for HF by 19% (13 RCTs, HR 0.81, 95% CI 0.68 to 0.98) but not all-cause mortality (13 RCTs; HR 0.87, 95% CI 0.75 to 1.01) or cardiovascular mortality (5 RCTs; OR 0.88, 95% CI 0.67 to 1.16). For all-cause mortality, there was a significant interaction between treatment strategy and age (p = 0.034, 11 RCTs; HR 0.70, 95% CI 0.53–0.92, patients

Excess body weight is thought to increase the risk of aggressive prostate cancer (PCa), although the biological mechanism is currently unclear. Body fatness is positively associated with a diminished cellular response to insulin and biomarkers of insulin signalling have been positively associated with PCa risk. We carried out a two-pronged systematic review of (a) the effect of reducing body fatness on insulin biomarker levels and (b) the effect of insulin biomarkers on PCa risk, to determine whether a reduction in body fatness could reduce PCa risk via effects on the insulin signalling pathway. We identified seven eligible randomised controlled trials of interventions designed to reduce body fatness which measured insulin biomarkers as an outcome, and six eligible prospective observational studies of insulin biomarkers and PCa risk. We found some evidence that a reduction in body fatness improved insulin sensitivity although our confidence in this evidence was low based on GRADE (Grading of Recommendations, Assessment, Development and Evaluations). We were unable to reach any conclusions on the effect of insulin sensitivity on PCa risk from the few studies included in our systematic review. A reduction in body fatness may reduce PCa risk via insulin signalling, but more high-quality evidence is needed before any conclusions can be reached regarding PCa.

Abstract Background When designing and analysing clinical trials, using previous relevant information, perhaps in the form of evidence syntheses, can reduce research waste. We conducted the INVEST (INVestigating the use of Evidence Synthesis in the design and analysis of clinical Trials) survey to summarise the current use of evidence synthesis in trial design and analysis, to capture opinions of trialists and methodologists on such use, and to understand any barriers. Methods Our sampling frame was all delegates attending the International Clinical Trials Methodology Conference in November 2015. Respondents were asked to indicate (1) their views on the use of evidence synthesis in trial design and analysis, (2) their own use during the past 10 years and (3) the three greatest barriers to use in practice. Results Of approximately 638 attendees of the conference, 106 (17%) completed the survey, half of whom were statisticians. Support was generally high for using a description of previous evidence, a systematic review or a meta-analysis in trial design. Generally, respondents did not seem to be using evidence syntheses as often as they felt they should. For example, only 50% (42/84 relevant respondents) had used a meta-analysis to inform whether a trial is needed compared with 74% (62/84) indicating that this is desirable. Only 6% (5/81 relevant respondents) had used a value of information analysis to inform sample size calculations versus 22% (18/81) indicating support for this. Surprisingly large numbers of participants indicated support for, and previous use of, evidence syntheses in trial analysis. For example, 79% (79/100) of respondents indicated that external information about the treatment effect should be used to inform aspects of the analysis. The greatest perceived barrier to using evidence synthesis methods in trial design or analysis was time constraints, followed by a belief that the new trial was the first in the area. Conclusions Evidence syntheses can be resource-intensive, but their use in informing the design, conduct and analysis of clinical trials is widely considered desirable. We advocate additional research, training and investment in resources dedicated to ways in which evidence syntheses can be undertaken more efficiently, offering the potential for cost savings in the long term.

Abstract Objective The objective of this review was to update a systematic review of associations between living near an animal feeding operation (AFO) and human health. Methods The MEDLINE® and MEDLINE® In-Process, Centre for Agricultural Biosciences Abstracts, and Science Citation Index databases were searched. Reference lists of included articles were hand-searched. Eligible studies reported exposure to an AFO and an individual-level human health outcome. Two reviewers performed study selection and data extraction. Results The search returned 3702 citations. Sixteen articles consisting of 10 study populations were included in the analysis. The health outcomes were lower and upper respiratory tracts, MRSA, other infectious disease, neurological, psychological, dermatological, otologic, ocular, gastrointestinal, stress and mood, and other non-infectious health outcomes. Most studies were observational and used prevalence measures of outcome. An association between Q fever risk and proximity to goat production was reported. Other associations were unclear. Risk of bias was serious or critical for most exposure-outcome associations. Multiplicity (i.e., a large number of potentially correlated outcomes and exposures assessed on the same study subjects) was common in the evidence base. Conclusions Few studies reported an association between surrogate clinical outcomes and AFO proximity for respiratory tract-related outcomes. There were no consistent dose-response relationships between surrogate clinical outcome and AFO proximity. A new finding was that Q fever in goats is likely associated with an increased Q fever risk in community members. The review results for the non-respiratory health outcomes were inconclusive because only a small number of studies were available or the between-study results were inconsistent. Systematic review registration PROSPERO CRD42014010521

Abstract Background Alcohol dependence is common and serious cause of social and physical harm. However, the optimal management of those with moderate and severe alcohol dependence in primary and community care after detoxification remains unclear. The aim of this review is to evaluate the effectiveness of interventions for maintaining abstinence in people with alcohol dependence following detoxification. Methods We will systematically search electronic databases and clinical trial registries for randomized controlled trials (RCTs) examining the effectiveness of pharmacological and/or psychosocial interventions for maintaining abstinence in recently detoxified, alcohol-dependent adults. The searches will be complemented by checking references and citations from included studies and other relevant systematic reviews. No limitation on language, year, or publication status will be applied. RCTs will be selected using prespecified criteria. Descriptive information, study characteristics, and results of eligible RCTs will be extracted. A revised version of the Cochrane Risk of Bias tool (RoB 2.0) will be used to assess the risk of bias in eligible RCTs. Results will be synthesized and analyzed using network meta-analysis (NMA). Overall strength of the evidence and publication bias will be evaluated. Subgroup and sensitivity analysis will also be performed. Discussion This network meta-analysis aims to appraise and summarize the total evidence of therapeutic interventions for alcohol-dependent patients that require support for detoxification and can be treated in the community. The evidence will determine which combination of interventions are most promising for current practice and further investigation. Systematic review registration PROSPERO CRD42016049779

Abstract Background Multiple corticosteroids and treatment regimens have been used as adjuncts in the treatment of septic shock. Qualitative and quantitative differences exist at cellular and tissular levels between the different drugs and their patterns of delivery. The objective of this study was to elucidate any differences between the drugs and their treatment regimens regarding outcomes for corticosteroid use in adult patients with septic shock. Methods Network meta-analysis of the data used for the recently conducted Cochrane review was performed. Studies that included children and were designed to assess respiratory function in pneumonia and acute respiratory distress syndrome, as well as cross-over studies, were excluded. Network plots were created for each outcome, and all analyses were conducted using a frequentist approach assuming a random-effects model. Results Complete data from 22 studies and partial data from 1 study were included. Network meta-analysis provided no clear evidence that any intervention or treatment regimen is better than any other across the spectrum of outcomes. There was strong evidence of differential efficacy in only one area: shock reversal. Hydrocortisone boluses and infusions were more likely than methylprednisolone boluses and placebo to result in shock reversal. Conclusions There was no clear evidence that any one corticosteroid drug or treatment regimen is more likely to be effective in reducing mortality or reducing the incidence of gastrointestinal bleeding or superinfection in septic shock. Hydrocortisone delivered as a bolus or as an infusion was more likely than placebo and methylprednisolone to result in shock reversal.

Objectives. Determine the optimal, licensed, first-line anticoagulant for prevention of ischemic stroke in patients with non-valvular atrial fibrillation (AF) in England and Wales from the UK National Health Service (NHS) perspective and estimate value to decision making of further research. Methods. We developed a cost-effectiveness model to compare warfarin (international normalized ratio target range 2–3) with directly acting (or non–vitamin K antagonist) oral anticoagulants (DOACs) apixaban 5 mg, dabigatran 150 mg, edoxaban 60 mg, and rivaroxaban 20 mg, over 30 years post treatment initiation. In addition to death, the 17-state Markov model included the events stroke, bleed, myocardial infarction, and intracranial hemorrhage. Input parameters were informed by systematic literature reviews and network meta-analysis. Expected value of perfect information (EVPI) and expected value of partial perfect information (EVPPI) were estimated to provide an upper bound on value of further research. Results. At willingness-to-pay threshold £20,000, all DOACs have positive expected incremental net benefit compared to warfarin, suggesting they are likely cost-effective. Apixaban has highest expected incremental net benefit (£7533), followed by dabigatran (£6365), rivaroxaban (£5279), and edoxaban (£5212). There was considerable uncertainty as to the optimal DOAC, with the probability apixaban has highest net benefit only 60%. Total estimated population EVPI was £17.94 million (17.85 million, 18.03 million), with relative effect between apixaban versus dabigatran making the largest contribution with EVPPI of £7.95 million (7.66 million, 8.24 million). Conclusions. At willingness-to-pay threshold £20,000, all DOACs have higher expected net benefit than warfarin but there is considerable uncertainty between the DOACs. Apixaban had the highest expected net benefit and greatest probability of having highest net benefit, but there is considerable uncertainty between DOACs. A head-to-head apixaban versus dabigatran trial may be of value.

Abstract Authors’ response to comments letter to the editor from Nachman KE et al.

Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search PubMed, ACL Anthology, arXiv, OpenAlex via EPPI-Reviewer, and the dblp computer science bibliography. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This living review update includes publications up to December 2022 and OpenAlex content up to March 2023. Results: 76 publications are included in this review. Of these, 64 (84%) of the publications addressed extraction of data from abstracts, while 19 (25%) used full texts. A total of 71 (93%) publications developed classifiers for randomised controlled trials. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. Data are available from 25 (33%), and code from 30 (39%) publications. Six (8%) implemented publicly available tools Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of literature review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. Between review updates, trends for sharing data and code increased strongly: in the base-review, data and code were available for 13 and 19% respectively, these numbers increased to 78 and 87% within the 23 new publications. Compared with the base-review, we observed another research trend, away from straightforward data extraction and towards additionally extracting relations between entities or automatic text summarisation. With this living review we aim to review the literature continually.

Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search MEDLINE, Institute of Electrical and Electronics Engineers (IEEE), arXiv, and the dblp computer science bibliography databases. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This iteration of the living review includes publications up to a cut-off date of 22 April 2020. Results: In total, 53 publications are included in this version of our review. Of these, 41 (77%) of the publications addressed extraction of data from abstracts, while 14 (26%) used full texts. A total of 48 (90%) publications developed and evaluated classifiers that used randomised controlled trials as the main target texts. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. A description of their datasets was provided by 49 publications (94%), but only seven (13%) made the data publicly available. Code was made available by 10 (19%) publications, and five (9%) implemented publicly available tools. Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of systematic review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. The lack of publicly available gold-standard data for evaluation, and lack of application thereof, makes it difficult to draw conclusions on which is the best-performing system for each data extraction target. With this living review we aim to review the literature continually.

Background: Researchers in evidence-based medicine cannot keep up with the amounts of both old and newly published primary research articles. Conducting and updating of systematic reviews is time-consuming. In practice, data extraction is one of the most complex tasks in this process. Exponential improvements in computational processing speed and data storage are fostering the development of data extraction models and algorithms. This, in combination with quicker pathways to publication, led to a large landscape of tools and methods for data extraction tasks. Objective: To review published methods and tools for data extraction to (semi)automate the systematic reviewing process. Methods: We propose to conduct a living review. With this methodology we aim to do monthly search updates, as well as bi-annual review updates if new evidence permits it. In a cross-sectional analysis we will extract methodological characteristics and assess the quality of reporting in our included papers. Conclusions: We aim to increase transparency in the reporting and assessment of machine learning technologies to the benefit of data scientists, systematic reviewers and funders of health research. This living review will help to reduce duplicate efforts by data scientists who develop data extraction methods. It will also serve to inform systematic reviewers about possibilities to support their data extraction.