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The DataFindR package provides tools leveraging Large Language Models (LLMs) to semi-automate the screening and data extraction steps for systematic reviews. It is designed to populate a project created and managed by the metawoRld package.
Key features include:
- Fetching metadata (Title/Abstract) for DOIs/PMIDs.
- Assessing study relevance using LLMs guided by project criteria stored in
metawoRld. - Extracting specific data points from full text using LLMs guided by a data schema stored in
metawoRld. - Caching results to avoid redundant API calls and costs.
- An interactive Shiny app for screening assistance.
Please see the Conceptual Overview vignette hosted by the metawoRld package for a detailed explanation of the entire living review pipeline and how DataFindR fits in. (Replace link with actual URL).
# Assuming DataFindR is on GitHub (replace 'andjar/DataFindR')# install.packages("remotes")remotes::install_github("andjar/DataFindR")
# DataFindR requires metawoRld# remotes::install_github("andjar/metawoRld")library(DataFindR)
# --- Prerequisites ---# 1. API key set (e.g., OPENAI_API_KEY in .Renviron, then restart R)# 2. A metawoRld project exists at the specified path# Example metawoRld setup (see metawoRld documentation):proj_path<- file.path(tempdir(), "assess_example_proj")
if(!dir.exists(proj_path)) {
try(metawoRld::create_metawoRld(proj_path, "Assess Example", "Desc",
inclusion_criteria=c("Human","Pregnancy","Cytokine"), exclusion_criteria=c("Animal","Review")))
}
# --- Assess a study ---pmid<-"31772108"# Example relevant PMIDif(dir.exists(proj_path)) {
assessment_result<- tryCatch(
df_assess_relevance(
identifier=pmid,
metawoRld_path=proj_path,
email="your.email@example.com"# Replace if needed
),
error=function(e) print(paste("Assessment failed:", e$message))
)
print(assessment_result)
} else {
print("Skipping assessment example: metawoRld project setup failed.")
}
# Or use the Shiny App# df_shiny_assess()# --- Clean up ---# unlink(proj_path, recursive = TRUE)