A Systematic Online Living Evidence Summary of experimental Alzheimer's disease research.

BACKGROUND: Despite extensive investment, the development of effective treatments for Alzheimer's disease (AD) has been largely unsuccessful. To improve translation, it is crucial to ensure the quality and reproducibility of foundational evidence generated from laboratory models. Systematic reviews play a key role in providing an unbiased overview of the evidence, assessing rigour and reporting, and identifying factors that influence reproducibility. However, the sheer pace of evidence generation is prohibitive to evidence synthesis and assessment. NEW METHOD: To address these challenges, we have developed AD-SOLES, an integrated workflow of automated tools that collect, curate, and visualise the totality of evidence from in vivo experiments. RESULTS: AD-SOLES is a publicly accessible interactive dashboard aiming to surface and expose data from in vivo experiments. It summarises the latest evidence, tracks reporting quality and transparency, and allows research users to easily locate evidence relevant to their specific research question. COMPARISON WITH EXISTING METHODS: Using automated screening methodologies within AD-SOLES, systematic reviews can begin at an accelerated starting point compared to traditional approaches. Furthermore, through text-mining approaches within the full-text of publications, users can identify research of interest using specific models, outcomes, or interventions without relying on details in the title and/or abstract. CONCLUSIONS: By automating the collection, curation, and visualisation of evidence from in vivo experiments, AD-SOLES addresses the challenges posed by the rapid pace of evidence generation. AD-SOLES aims to offer guidance for research improvement, reduce research waste, highlight knowledge gaps, and support informed decision making for researchers, funders, patients, and the public.

The Automated Systematic Search Deduplicator (ASySD): a rapid, open-source, interoperable tool to remove duplicate citations in biomedical systematic reviews.

BACKGROUND: Researchers performing high-quality systematic reviews search across multiple databases to identify relevant evidence. However, the same publication is often retrieved from several databases. Identifying and removing such duplicates ("deduplication") can be extremely time-consuming, but failure to remove these citations can lead to the wrongful inclusion of duplicate data. Many existing tools are not sensitive enough, lack interoperability with other tools, are not freely accessible, or are difficult to use without programming knowledge. Here, we report the performance of our Automated Systematic Search Deduplicator (ASySD), a novel tool to perform automated deduplication of systematic searches for biomedical reviews. METHODS: We evaluated ASySD's performance on 5 unseen biomedical systematic search datasets of various sizes (1845-79,880 citations). We compared the performance of ASySD with EndNote's automated deduplication option and with the Systematic Review Assistant Deduplication Module (SRA-DM). RESULTS: ASySD identified more duplicates than either SRA-DM or EndNote, with a sensitivity in different datasets of 0.95 to 0.99. The false-positive rate was comparable to human performance, with a specificity of > 0.99. The tool took less than 1 h to identify and remove duplicates within each dataset. CONCLUSIONS: For duplicate removal in biomedical systematic reviews, ASySD is a highly sensitive, reliable, and time-saving tool. It is open source and freely available online as both an R package and a user-friendly web application.

Impact of COVID-19 mitigations on anxiety and depression amongst university students: A systematic review and meta-analysis.

Background: While much research has addressed mental health concerns related to the coronavirus disease 2019 (COVID-19) pandemic, there remains a scarcity of studies specifically exploring the changes in anxiety and depression among university students before and after the implementation of COVID-19 mitigation measures. Methods: In this systematic review and meta-analysis, we searched databases including MEDLINE (Ovid), EMBASE (Ovid), PsycINFO (Ovid), CINAHL (EBSCO), ERIC (EBSCO), the WHO COVID-19 database, Scopus, and Science Citation Index (Web of Science) as of 15 February 2023. We included studies that used a validated tool to measure changes in anxiety or depression at two distinct time points - before (T1) and during (T2); during (T2) and after (T3); or before (T1) and after (T3) COVID-19 mitigation. The quality of studies was assessed using an adapted Joanna Briggs Institute Checklist for longitudinal studies. Utilising random-effects models, we synthesised changes in continuous outcomes as standardised mean difference (SMD) with 95% confidence interval (CI) and binary outcomes as risk difference (RD) with 95% CI. Results: In total, 15 studies were included in this review, with eight of moderate and seven of high quality. In most of the included studies (n = 13), the majority of participants were women. Eleven studies analysed mental health outcomes between T1 and T2 of COVID-19 mitigations. Continuous symptom changes were a minimal or small improvement for anxiety (SMD = -0.03, 95% CI = -0.24 to 0.19, I2 = 90%); but worsened for depression (SMD = 0.26, 95% CI = -0.01 to 0.62). However, the proportions of students reporting moderate-to-severe symptoms, defined by specific cut-offs, increased during COVID-19 mitigation measures for both anxiety (RD = 0.17, 95% CI = -0.04 to 0.38, I2 = 95%) and depression (RD = 0.12, 95% CI = 0.03 to 0.22, I2 = 72%). Sensitivity analyses, which distinguished between baseline periods based on awareness of COVID-19, demonstrated an exacerbation of both symptoms when comparing the period before the global awareness of the COVID-19 outbreak (before December 2019) with the period during the implementation of mitigation measures. Conclusions: Mental health outcomes, especially depressive symptoms, were observed to worsen in university students during COVID-19 mitigations. Despite considerable heterogeneity requiring careful interpretation of results, the impact of COVID-19 mitigations on mental health in university students is evident. Registration: PROSPERO (CRD42021266889).

Systematic online living evidence summaries: emerging tools to accelerate evidence synthesis.

Systematic reviews and meta-analysis are the cornerstones of evidence-based decision making and priority setting. However, traditional systematic reviews are time and labour intensive, limiting their feasibility to comprehensively evaluate the latest evidence in research-intensive areas. Recent developments in automation, machine learning and systematic review technologies have enabled efficiency gains. Building upon these advances, we developed Systematic Online Living Evidence Summaries (SOLES) to accelerate evidence synthesis. In this approach, we integrate automated processes to continuously gather, synthesise and summarise all existing evidence from a research domain, and report the resulting current curated content as interrogatable databases via interactive web applications. SOLES can benefit various stakeholders by (i) providing a systematic overview of current evidence to identify knowledge gaps, (ii) providing an accelerated starting point for a more detailed systematic review, and (iii) facilitating collaboration and coordination in evidence synthesis.

Improving the quality of toxicology and environmental health systematic reviews: What journal editors can do.

Systematic reviews are fast increasing in prevalence in the toxicology and environmental health literature. However, how well these complex research projects are being conducted and reported is unclear. Since editors have an essential role in ensuring the scientific quality of manuscripts being published in their journals, a workshop was convened where editors, systematic review practitioners, and research quality control experts could discuss what editors can do to ensure the systematic reviews they publish are of sufficient scientific quality. Interventions were explored along four themes: setting standards; reviewing protocols; optimizing editorial workflows; and measuring the effectiveness of editorial interventions. In total, 58 editorial interventions were proposed. Of these, 26 were shortlisted for being potentially effective, and 5 were prioritized as short-term actions that editors could relatively easily take to improve the quality of published systematic reviews. Recent progress in improving systematic reviews is summarized, and outstanding challenges to further progress are highlighted.

Design of Meta-Analysis Studies.

Any given research claim can be made with a degree of confidence that a phenomenon is present, with an estimate of the precision of the observed effects and a prediction of the extent to which the findings might hold true under different experimental or real-world conditions. In some situations, the certainty and precision obtained from a single study are sufficient reliably to inform future research decisions. However, in other situations greater certainty is required. This might be the case where a substantial research investment is planned, a pivotal claim is to be made or the launch of a clinical trial programme is being considered. Under these circumstances, some form of summary of findings across studies may be helpful.Summary estimates can describe findings from exploratory (observational) or hypothesis testing experiments, but importantly, the creation of such summaries is, in itself, observational rather than experimental research. The process is therefore particularly at risk from selective identification of literature to be included, and this can be addressed using systematic search strategies and pre-specified criteria for inclusion and exclusion against which possible contributing data will be assessed. This characterises a systematic review (in contrast to nonsystematic or narrative reviews). In meta-analysis, there is an attempt to provide a quantitative summary of such research findings.

A randomised controlled trial of an Intervention to Improve Compliance with the ARRIVE guidelines (IICARus).

BACKGROUND: The ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines are widely endorsed but compliance is limited. We sought to determine whether journal-requested completion of an ARRIVE checklist improves full compliance with the guidelines. METHODS: In a randomised controlled trial, manuscripts reporting in vivo animal research submitted to PLOS ONE (March-June 2015) were randomly allocated to either requested completion of an ARRIVE checklist or current standard practice. Authors, academic editors, and peer reviewers were blinded to group allocation. Trained reviewers performed outcome adjudication in duplicate by assessing manuscripts against an operationalised version of the ARRIVE guidelines that consists 108 items. Our primary outcome was the between-group differences in the proportion of manuscripts meeting all ARRIVE guideline checklist subitems. RESULTS: We randomised 1689 manuscripts (control: n = 844, intervention: n = 845), of which 1269 were sent for peer review and 762 (control: n = 340; intervention: n = 332) accepted for publication. No manuscript in either group achieved full compliance with the ARRIVE checklist. Details of animal husbandry (ARRIVE subitem 9b) was the only subitem to show improvements in reporting, with the proportion of compliant manuscripts rising from 52.1 to 74.1% (X 2 = 34.0, df = 1, p = 2.1 × 10-7) in the control and intervention groups, respectively. CONCLUSIONS: These results suggest that altering the editorial process to include requests for a completed ARRIVE checklist is not enough to improve compliance with the ARRIVE guidelines. Other approaches, such as more stringent editorial policies or a targeted approach on key quality items, may promote improvements in reporting.

Animal models of chemotherapy-induced peripheral neuropathy: A machine-assisted systematic review and meta-analysis.

We report a systematic review and meta-analysis of research using animal models of chemotherapy-induced peripheral neuropathy (CIPN). We systematically searched 5 online databases in September 2012 and updated the search in November 2015 using machine learning and text mining to reduce the screening for inclusion workload and improve accuracy. For each comparison, we calculated a standardised mean difference (SMD) effect size, and then combined effects in a random-effects meta-analysis. We assessed the impact of study design factors and reporting of measures to reduce risks of bias. We present power analyses for the most frequently reported behavioural tests; 337 publications were included. Most studies (84%) used male animals only. The most frequently reported outcome measure was evoked limb withdrawal in response to mechanical monofilaments. There was modest reporting of measures to reduce risks of bias. The number of animals required to obtain 80% power with a significance level of 0.05 varied substantially across behavioural tests. In this comprehensive summary of the use of animal models of CIPN, we have identified areas in which the value of preclinical CIPN studies might be increased. Using both sexes of animals in the modelling of CIPN, ensuring that outcome measures align with those most relevant in the clinic, and the animal's pain contextualised ethology will likely improve external validity. Measures to reduce risk of bias should be employed to increase the internal validity of studies. Different outcome measures have different statistical power, and this can refine our approaches in the modelling of CIPN.