Technological advances in preclinical meta-research

Bannach‐Brown, Alexandra; Hair, Kaitlyn; Bahor, Zsanett; Soliman, Nadia; Macleod, Malcolm; Liao, Jing

doi:10.1136/bmjos-2020-100131

Cited by 18 publications

(13 citation statements)

References 48 publications

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“…All of these issues, however, will only be resolved when systematic iteration between data synthesis and experimentation becomes more common in discovery science. This requires engaging researchers more widely in systematic reviews, as well as making these more efficient through automated tools (Bannach-Brown et al, 2021) and more structured primary data. It also involves building up infrastructure for confirmatory research that can go beyond the work of individual labs and validate meta-analytical findings empirically (Amaral & Neves, 2021).…”

Section: Discussionmentioning

confidence: 99%

A meta-analysis of the effect of protein synthesis inhibitors on rodent fear conditioning

Carneiro

Amorim

Amaral

2022

Preprint

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Systematic reviews and meta-analyses have been increasingly recognized for their potential value in pre-clinical research, but their multiple applications have not been extensively explored in behavioral neuroscience. In this work, we studied protein synthesis inhibition, a classic intervention used to disrupt fear learning, reconsolidation, and extinction in rodents, to explore how meta-analyses can identify potential moderators of its effect. We initially performed separate meta-analyses for different injection sites (systemic, hippocampus, amygdala, and cerebral ventricles) and target sessions (training, reconsolidation and extinction) to evaluate the effect of the intervention in various scenarios. Heterogeneity was further investigated by uni- and multivariable meta-regression using models aggregating various sites, with article or research group as additional levels. We detected robust effects of protein synthesis inhibitors on training and reconsolidation, but not on extinction, possibly due to the lower number of studies on the latter. Our analyses identified some well-established moderators, such as injection timing for training interventions and reexposure duration for reactivation interventions. However, other factors proposed as boundary conditions for reconsolidation, such as training strength and memory age, were not associated with effect size across the literature. Additionally, our findings suggest some underexplored associations, such as a larger susceptibility of cued fear conditioning to reconsolidation when compared to the contextual version of the task. While our results point to the value of meta-analyses in consolidating findings from the literature, we believe that associations suggested by data synthesis should ideally be verified by well-powered, rigorous confirmatory experiments.

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Section: Discussionmentioning

confidence: 99%

A meta-analysis of the effect of protein synthesis inhibitors on rodent fear conditioning

Carneiro

Amorim

Amaral

2022

Preprint

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“…To economize time spent on deduplication issues, several tools were developed to simplify procedures and increase efficiency [7][8][9][10][11], which require technical proficiency and manual interventions [12]. For example, EndNote [13] is a widely used reference management tool [14] (among others such as Mendeley, RefWorks, and Zotero) offering an integrated deduplication feature, which usually demands careful, multistep configuration procedures [12,15,16].…”

Section: Open Accessmentioning

confidence: 99%

Reducing systematic review burden using Deduklick: a novel, automated, reliable, and explainable deduplication algorithm to foster medical research

et al. 2022

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Background Identifying and removing reference duplicates when conducting systematic reviews (SRs) remain a major, time-consuming issue for authors who manually check for duplicates using built-in features in citation managers. To address issues related to manual deduplication, we developed an automated, efficient, and rapid artificial intelligence-based algorithm named Deduklick. Deduklick combines natural language processing algorithms with a set of rules created by expert information specialists. Methods Deduklick’s deduplication uses a multistep algorithm of data normalization, calculates a similarity score, and identifies unique and duplicate references based on metadata fields, such as title, authors, journal, DOI, year, issue, volume, and page number range. We measured and compared Deduklick’s capacity to accurately detect duplicates with the information specialists’ standard, manual duplicate removal process using EndNote on eight existing heterogeneous datasets. Using a sensitivity analysis, we manually cross-compared the efficiency and noise of both methods. Discussion Deduklick achieved average recall of 99.51%, average precision of 100.00%, and average F1 score of 99.75%. In contrast, the manual deduplication process achieved average recall of 88.65%, average precision of 99.95%, and average F1 score of 91.98%. Deduklick achieved equal to higher expert-level performance on duplicate removal. It also preserved high metadata quality and drastically reduced time spent on analysis. Deduklick represents an efficient, transparent, ergonomic, and time-saving solution for identifying and removing duplicates in SRs searches. Deduklick could therefore simplify SRs production and represent important advantages for scientists, including saving time, increasing accuracy, reducing costs, and contributing to quality SRs.

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“…Recently, several groups have sought to facilitate faster evidence synthesis through the application of automation technologies (Bannach-Brown et al, 2021). By incorporating natural language processing and other machine-assisted techniques, the human effort and time required to complete each step of the SR process can be dramatically reduced.…”

Section: Automation and "Living" Systematic Reviewsmentioning

confidence: 99%

Systematic Online Living Evidence Summaries: emerging tools to accelerate evidence synthesis

Hair¹,

Wilson²,

Wong³

et al. 2022

Preprint

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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.

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Technological advances in preclinical meta-research

Cited by 18 publications

References 48 publications

A meta-analysis of the effect of protein synthesis inhibitors on rodent fear conditioning

A meta-analysis of the effect of protein synthesis inhibitors on rodent fear conditioning

Reducing systematic review burden using Deduklick: a novel, automated, reliable, and explainable deduplication algorithm to foster medical research

Systematic Online Living Evidence Summaries: emerging tools to accelerate evidence synthesis

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