A living systematic review of prevention, diagnosis and treatments for every disease.
When conducting a living systematic review, checking if new evidence is published continuously is essential. Automated Search automatically executes your search strategy in bibliographic databases using application programming interface integration. We have Automated Search integrated for PubMed and are working on integrating more databases. When new studies that fit your search strategy become available, they will automatically appear in the screening workflow.
Real-time collaboration with reviewers is critical when conducting a systematic review. When starting the review, you can configure customized exclusion criteria. You can choose to do the independent blinded screening. It is easy to resolve conflicts when they arrive. A machine-learning algorithm can be used to classify randomized controlled trials based on the abstracts.
Setting up a data extraction form is often difficult, especially for network meta-analysis. You can easily pre-configure the data extraction form in the web application to fit your needs. While conducting a living systematic review, adding data is sometimes necessary due to unforeseen circumstances. You can edit the data extraction form during the living systematic review. You can also do data extraction alone or with two reviewers independently with blinding.
Using integration with Chat GPT (using OpenAI API), we make it possible to receive GPT-generated recommendations of values and sentences during data extraction for all sort of data items like population characteristics, percentage of men and women in a study, mean age of the participants in the trial etc. Try it out in our demo: app.pitts.ai
Every human life has extraordinary value and is worth saving. In the past few years, the workload to synthesize reliable, unbiased, up-to-date, and complete health information has rapidly increased due to increased publications in biomedical literature. The most optimal treatment for nearly all medical conditions is unknown due to an incomplete overview of the evidence. Even less is known about disease prevention and healthy living. The incomplete overview of evidence undoubtedly resulted in non-optimal outcomes for many patients. The answer is living systematic reviews and (network) meta-analyses of diagnosis, treatments, and prevention for every disease. Our mission is to facilitate trust in medicine by building the technical infrastructure to make rigorous, complete, and up-to-date evidence synthesis possible.
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for evidence-based medicine
We are a small but dedicated team of software engineers working on building the future of evidence-based medicine.