ORCID Profile
0000-0003-0180-7343
Current Organisation
The University of Edinburgh
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Publisher: European Association for Health Information and Libraries EAHIL
Date: 24-06-2021
DOI: 10.32384/JEAHIL17465
Abstract: Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence.
Publisher: Cold Spring Harbor Laboratory
Date: 19-07-2018
DOI: 10.1101/370874
Abstract: 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. In a randomised controlled trial, manuscripts reporting in vivo animal research submitted to PLOS ONE (March-June 2015) were allocated to either requested completion of an ARRIVE checklist or current standard practice. We measured the change in proportion of manuscripts meeting all ARRIVE guideline checklist items between groups. We randomised 1,689 manuscripts, 1,269 were sent for peer review and 762 accepted for publication. The request to complete an ARRIVE checklist had no effect on full compliance with the ARRIVE guidelines. Details of animal husbandry (ARRIVE sub-item 9a) was the only item to show improved reporting, from 52.1% to 74.1% (X 2 =34.0, df=1, p=2.1×10 −7 ). These results suggest that other approaches are required to secure greater implementation of the ARRIVE guidelines.
Publisher: Portico
Date: 07-2021
Publisher: Portico
Date: 02-2019
Publisher: Center for Open Science
Date: 18-08-2022
Abstract: 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.
Publisher: Cold Spring Harbor Laboratory
Date: 30-01-2018
DOI: 10.1101/256776
Abstract: Meta-analysis is increasingly used to summarise the findings identified in systematic reviews of animal studies modelling human disease. Such reviews typically identify a large number of in idually small studies, testing efficacy under a variety of conditions. This leads to substantial heterogeneity, and identifying potential sources of this heterogeneity is an important function of such analyses. However, the statistical performance of different approaches (normalised compared with standardised mean difference estimates of effect size stratified meta-analysis compared with meta-regression) is not known. Using data from 3116 experiments in focal cerebral ischaemia to construct a linear model predicting observed improvement in outcome contingent on 25 independent variables. We used stochastic simulation to attribute these variables to simulated studies according to their prevalence. To ascertain the ability to detect an effect of a given variable we introduced in addition this “variable of interest” of given prevalence and effect. To establish any impact of a latent variable on the apparent influence of the variable of interest we also introduced a “latent confounding variable” with given prevalence and effect, and allowed the prevalence of the variable of interest to be different in the presence and absence of the latent variable. Generally, the normalised mean difference (NMD) approach had higher statistical power than the standardised mean difference (SMD) approach. Even when the effect size and the number of studies contributing to the meta-analysis was small, there was good statistical power to detect the overall effect, with a low false positive rate. For detecting an effect of the variable of interest, stratified meta-analysis was associated with a substantial false positive rate with NMD estimates of effect size, while using an SMD estimate of effect size had very low statistical power. Univariate and multivariable meta-regression performed substantially better, with low false positive rate for both NMD and SMD approaches power was higher for NMD than for SMD. The presence or absence of a latent confounding variables only introduced an apparent effect of the variable of interest when there was substantial asymmetry in the prevalence of the variable of interest in the presence or absence of the confounding variable. In meta-analysis of data from animal studies, NMD estimates of effect size should be used in preference to SMD estimates, and meta-regression should, where possible, be chosen over stratified meta-analysis. The power to detect the influence of the variable of interest depends on the effect of the variable of interest and its prevalence, but unless effects are very large adequate power is only achieved once at least 100 experiments are included in the meta-analysis.
Publisher: Public Library of Science (PLoS)
Date: 20-05-2019
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Kaitlyn Hair.