Optimizing ASReview simulations
Optimizing ASReview simulations with multiprocessing solutions for ‘light-data’ and ‘heavy-data’ users via a Kubernetes cluster.
Optimizing ASReview simulations with multiprocessing solutions for ‘light-data’ and ‘heavy-data’ users via a Kubernetes cluster.
The FORAS project will replicate and extend an original review integrating advanced machine-learning techniques via the OpenAlex database.
In this study, we addressed the issue of the lack of replicability of systematic reviews datasets. We used a case study format and developed a procedure to optimize and finalize the by rule imperfect reconstructed dataset.
ASReview conducted a simulation study on risk analysis documents to evaluate the time-benefit for the Royal Dutch Pharmacists Association.
The MegaMeta project is a large scale project to review factors that contribute to substance use, anxiety and depressive disorders. Read more information on the search and screening protocol, hyperparameter tuning and post-processing used in this post.
After manually screening 5050 studies, Smid et al included only 27 studies for their review. In the current study, both the Bayesian and logistic regression models found more than 80% of relevant publications after screening only 10% of all publications.