What is high-quality evidence?
Over 1 million new scientific articles are published every year. With conflicting results published regularly, it can be hard to gain an overview over a field. Systematic literature reviews and meta-analyses are the best ways to get a scientifically robust, non-biased overview, but these are usually labor intensive and timely to produce. With Silvi.ai, you can quickly and easily create both.
What are systematic literature reviews?
A systematic literature review is a systematically collected, transparent overview of literature on a scientific topic. It reviews all the literature published on a topic (often within a specific time frame) to avoid selection bias. Especially within social sciences and new, up-and-coming areas, a systematic literature review gives the most thorough overview.
How can Silvi help you with systematic literature reviews?
Silvi.ai helps you create systematic literature reviews that live up to Cochrane and EUnetHTA guidelines, ensuring the highest quality of data. The systematic literature reviews are always fully updated, as Silvi is directly connected to literature engines, so your reviews stay relevant without having to start over. The systematic literature review can be adjusted for the necessary level of approval with blinded decision making. Whether working in pharma, biotech, or health tech/device, as well as with medical authorities, systematic literature reviews are the best tool to get a full overview of a scientific area.
What are meta-analyses?
A meta-analysis is a transparent statistical analysis based on multiple scientific studies on a topic, leading to a pooled estimate of a given effect across studies. The goal of a meta-analysis is to calculate the tendencies in scientific literature. Meta-analyses are considered the golden standard of evidence-based medicine, giving us the highest quality evidence within the field – they are the best way to get an accurate overview.
How can Silvi help you with meta-analyses?
A meta-analysis is a great tool to get an overview of, i.e., the effects and side effects of a drug or treatment. Silvi.ai helps develop meta-analyses that live up to the highest scientific standards: Cochrane and EUnetHTA guidelines. With the direct connection to sources such as PubMed and ClinicalTrials.gov, the semiautomatic extraction of data, and the integration of the statistical software R for illustrative forest plots, Silvi can drastically reduce the time needed to produce solid evidence.
To create a meta-analysis, Silvi automatically extracts data from the studies included in the screening phase. The Silvi document parser will parse the PDF of the study using OCR and detect which tables report effect measurements. Then Silvi uses machine learning to analyze the tables and extract the available outcomes such as endpoint, population, and efficacy. Currently Silvi can detect 50% of all outcomes. Silvi is directly connected to the statistical analysis tool “R”, ensuring a quick and integrated statistical processing of data.