Preclinical systematic reviews (SRs) and meta-analyses (MAs) are important research activities to address the translational challenges of pain research. SRs provide empirical evidence to gain knowledge, inform future research agendas and grant applications concurrent to developing researchers’ professional skills.
SRs are an effective approach to consolidating high volume, rapidly accruing and often conflicting research on a specific topic. Designed to address a specific research question, SRs use predefined methods to identify, select and critically appraise all available and relevant literature to answer that question in an unbiased manner [18]. This structured approach distinguishes SRs from narrative reviews. Where appropriate a MA can follow, whereby quantitative data are extracted and statistical techniques are used to summarise the outputs. Together, a SR and MA can be conducted to assess the quality of experimental design, conduct, analysis and reporting and the reliability of all available and relevant data [45].
Through decades of innovation by the Cochrane Collaboration and others, SRs and MAs now lie at the centre of clinical evidence. The information provided has fundamentally revolutionised clinical medicine at all levels from informing policy and funding decisions to determining optimal treatments for individual patients. Before a clinical research project or funding application, it is best practise to conduct a SR to ascertain what is already known and to identify knowledge gaps.
In the preclinical setting SRs are relatively novel, partially because of inherent complexities and resource requirements for processing the large number and diverse preclinical publications; paradoxically a strong justification for SRs because they provide the means to synthesise evidence from heterogeneous studies. In some fields they are gaining popularity (e.g. stroke [37]) and feasibility is improving with technical advances e.g. online review software, machine learning and text mining [3]. However, it is important to highlight that not all SRs require machine learning expertise: research questions can be defined based upon capacity, SR software are free and widely accessible, and large-scale SRs constantly seek help from interested researchers who can learn as they participate.
The aim of this review is to highlight the exciting possibilities a preclinical SR can bring to your research toolkit, demonstrate the importance of preclinical SRs in generating empirical evidence to aid robust experimental design, inform research strategy and support funding applications. We provide guidance and signpost resources to conduct a preclinical SR.