Using GPT-4 for Title and Abstract Screening in a Literature Review of Public Policies

Introduction

We describe the first known use of large language models (LLMs) to screen titles and abstracts in a review of public policy literature. Our objective was to assess the percentage of articles GPT-4 recommended for exclusion that should have been included ("false exclusion rate").

Methods

We used GPT-4 to exclude articles from a database for a literature review of quantitative evaluations of federal and state policies addressing the opioid crisis. We exported our bibliographic database to a CSV file containing titles, abstracts, and keywords and asked GPT-4 to recommend whether to exclude each article. We conducted a preliminary testing of these recommendations using a subset of articles and a final test on a sample of the entire database. We designated a false exclusion rate of 10% as an adequate performance threshold.

Results

GPT-4 recommended excluding 41,742 of the 43,480 articles (96%) containing an abstract. Our preliminary test identified only one false exclusion; our final test identified no false exclusions, yielding an estimated false exclusion rate of 0.00 [0.00, 0.05]. Fewer than 1%—417 of the 41,742 articles—were incorrectly excluded. After manually assessing the eligibility of all remaining articles, we identified 608 of the 1738 articles that GPT-4 did not exclude: 65% of the articles recommended for inclusion should have been excluded.

Discussion/Conclusions

GPT-4 performed well at recommending articles to exclude from our literature review, resulting in substantial time and cost savings. A key limitation is that we did not use GPT-4 to determine inclusions, nor did our model perform well on this task. However, GPT-4 dramatically reduced the number of articles requiring review. Systematic reviewers should conduct performance evaluations to ensure that an LLM meets a minimally acceptable quality standard before relying on its recommendations.