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Background and Objectives Systematic reviews (SRs) are time and resource intensive, requiring approximately 1 year from protocol registration to submission for publication. Our aim was to describe the process, facilitators, and barriers to completing the first 2-week full SR. Study Design and Setting We systematically reviewed evidence of the impact of increased fluid intake, on urinary tract infection (UTI) recurrence, in individuals at risk for UTIs. The review was conducted by experienced systematic reviewers with complementary skills (two researcher clinicians, an information specialist, and an epidemiologist), using Systematic Review Automation tools, and blocked off time for the duration of the project. The outcomes were time to complete the SR, time to complete individual SR tasks, facilitators and barriers to progress, and peer reviewer feedback on the SR manuscript. Times to completion were analyzed quantitatively (minutes and calendar days); facilitators and barriers were mapped onto the Theoretical Domains Framework; and peer reviewer feedback was analyzed quantitatively and narratively. Results The SR was completed in 61 person-hours (9 workdays; 12 calendar days); accepted version of the manuscript required 71 person-hours. Individual SR tasks ranged from 16 person-minutes (deduplication of search results) to 461 person-minutes (data extraction). The least time-consuming SR tasks were obtaining full-texts, searches, citation analysis, data synthesis, and deduplication. The most time-consuming tasks were data extraction, write-up, abstract screening, full-text screening, and risk of bias. Facilitators and barriers mapped onto the following domains: knowledge; skills; memory, attention, and decision process; environmental context and resources; and technology and infrastructure. Two sets of peer reviewer feedback were received on the manuscript: the first included 34 comments requesting changes, 17 changes were made, requiring 173 person-minutes; the second requested 13 changes, and eight were made, requiring 121 person-minutes. Conclusion A small and experienced systematic reviewer team using Systematic Review Automation tools who have protected time to focus solely on the SR can complete a moderately sized SR in 2 weeks.

Synthesizing evidence is an essential part of scientific progress, but it is often done in a slow and uncoordinated manner, sometimes producing misleading conclusions. Here, we propose the idea of an ‘open synthesis community’ to resolve this pressing issue.

Preclinical research is a vital step in the drug discovery pipeline and more generally in helping to better understand human disease aetiology and its management. Systematic reviews (SRs) can be powerful in summarising and appraising this evidence concerning a specific research question, to highlight areas of improvements, areas for further research and areas where evidence may be sufficient to take forward to other research domains, for instance clinical trial. Guidance and tools for preclinical research synthesis remain limited despite their clear utility. We aimed to create an online end-to-end platform primarily for conducting SRs of preclinical studies, that was flexible enough to support a wide variety of experimental designs, was adaptable to different research questions, would allow users to adopt emerging automated tools and support them during their review process using best practice. In this article, we introduce the Systematic Review Facility (https://syrf.org.uk), which was launched in 2016 and designed to support primarily preclinical SRs from small independent projects to large, crowdsourced projects. We discuss the architecture of the app and its features, including the opportunity to collaborate easily, to efficiently manage projects, to screen and annotate studies for important features (metadata), to extract outcome data into a secure database, and tailor these steps to each project. We introduce how we are working to leverage the use of automation tools and allow the integration of these services to accelerate and automate steps in the systematic review workflow.

International audience; The COVID-19 pandemic has thrust preprints into the spotlight, highlighting their advantages and disadvantages. The lack of peer review allows publication to occur with unprecedented speed, but this has raised concerns among biomedical scientists about the quality of the reported research. The study had three primary objectives in regard to COVID-19 preprints: 1. Test the feasibility of automatically and publicly evaluating preprints on a large scale; 2. Assess the prevalence of common quality problems in preprints; and 3. Compare the quality of preprints to published papers. While a substantial fraction (36.2%) of preprints addressed study limitations, the proportion that met other quality criteria was much lower, with only 20% addressing sex as a variable, ~14% sharing open code or data, 7.6% including non-colorblind safe images, and 7.3% showing misleading bar graphs. Both authors and non-authors interacted with the automated Tweets containing reports for preprints. This project shows that it is feasible to conduct large-scale automated screening of preprints for common quality criteria and provide feedback to study authors and readers before publication. These reports can publicly raise awareness about factors that affect study quality and reproducibility, while helping authors to present their research in a more transparent and reproducible manner.

Background Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm comparable to human screening that can reduce human resources required for carrying out this step of a systematic review. Methods We applied ML approaches to a broad systematic review of animal models of depression at the citation screening stage. We tested two independently developed ML approaches which used different classification models and feature sets. We recorded the performance of the ML approaches on an unseen validation set of papers using sensitivity, specificity and accuracy. We aimed to achieve 95% sensitivity and to maximise specificity. The classification model providing the most accurate predictions was applied to the remaining unseen records in the dataset and will be used in the next stage of the preclinical biomedical sciences systematic review. We used a cross-validation technique to assign ML inclusion likelihood scores to the human screened records, to identify potential errors made during the human screening process (error analysis). Results ML approaches reached 98.7% sensitivity based on learning from a training set of 5749 records, with an inclusion prevalence of 13.2%. The highest level of specificity reached was 86%. Performance was assessed on an independent validation dataset. Human errors in the training and validation sets were successfully identified using the assigned inclusion likelihood from the ML model to highlight discrepancies. Training the ML algorithm on the corrected dataset improved the specificity of the algorithm without compromising sensitivity. Error analysis correction leads to a 3% improvement in sensitivity and specificity, which increases precision and accuracy of the ML algorithm. Conclusions This work has confirmed the performance and application of ML algorithms for screening in systematic reviews of preclinical animal studies. It has highlighted the novel use of ML algorithms to identify human error. This needs to be confirmed in other reviews with different inclusion prevalence levels, but represents a promising approach to integrating human decisions and automation in systematic review methodology. Electronic supplementary material The online version of this article (10.1186/s13643-019-0942-7) contains supplementary material, which is available to authorized users.

Mobile phones have become an integral part of modern society. As possible breeding grounds for microbial organisms, these constitute a potential global public health risk for microbial transmission.Scoping review of literature examining microbial's presence on mobile phones in both health care (HC) and community settings.A search (PubMedGoogleScholar) was conducted from January 2005-December 2019 to identify English language studies. Studies were included if samples from mobile phones were tested for bacteria, fungi, and/or viruses; and if the sampling was carried out in any HC setting, and/or within the general community. Any other studies exploring mobile phones that did not identify specific microorganisms were excluded.A total of 56 studies were included (from 24 countries). Most studies identified the presence of bacteria (54/56), while 16 studies reported the presence of fungi. One study focused solely on RNA viruses. Staphylococcus aureus, and Coagulase-Negative Staphylococci were the most numerous identified organisms present on mobile phones. These two species and Escherichia coli were present in over a third of studies both in HC and community samples. Methicillin-resistant S. aureus, Acinetobacter sp., and Bacillus sp. were present in over a third of the studies in HC settings.While this scoping review of literature regarding microbial identification on mobile phones in HC and community settings did not directly address the issue of SARS-CoV-2 responsible for COVID-19, this work exposes the possible role of mobile phones as a 'Trojan horse' contributing to the transmission of microbial infections in epidemics and pandemics.

Objective To determine whether a change in editorial policy, including the implementation of a checklist, has been associated with improved reporting of measures which might reduce the risk of bias.Methods The study protocol has been published at doi: 10.1007/s11192-016-1964-8.Design Observational cohort study.Population Articles describing research in the life sciences published in Nature journals, submitted after 1 May 2013.Intervention Mandatory completion of a checklist during manuscript revision.Comparators (1) Articles describing research in the life sciences published in Nature journals, submitted before May 2013; and (2) similar articles in other journals matched for date and topic.Primary outcome The primary outcome is change in the proportion of Nature articles describing in vivo research published before and after May 2013 reporting the ‘Landis 4’ items (randomisation, blinding, sample size calculation and exclusions). We included 448 Nature Publishing Group (NPG) articles (223 published before May 2013, and 225 after) identified by an individual hired by NPG for this specific task, working to a standard procedure; and an independent investigator used PubMed ‘Related Citations’ to identify 448 non-NPG articles with a similar topic and date of publication from other journals; and then redacted all articles for time-sensitive information and journal name. Redacted articles were assessed by two trained reviewers against a 74-item checklist, with discrepancies resolved by a third.Results 394 NPG and 353 matching non-NPG articles described in vivo research. The number of NPG articles meeting all relevant Landis 4 criteria increased from 0/203 prior to May 2013 to 31/181 (16.4%) after (two-sample test for equality of proportions without continuity correction, Χ²=36.2, df=1, p=1.8×10−9). There was no change in the proportion of non-NPG articles meeting all relevant Landis 4 criteria (1/164 before, 1/189 after). There were more substantial improvements in the individual prevalences of reporting of randomisation, blinding, exclusions and sample size calculations for in vivo experiments, and less substantial improvements for in vitro experiments.Conclusion There was an improvement in the reporting of risks of bias in in vivo research in NPG journals following a change in editorial policy, to a level that to our knowledge has not been previously observed. However, there remain opportunities for further improvement.This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.