Citation map · Study

Human-Comparable Sensitivity of Large Language Models in Identifying Eligible Studies Through Title and Abstract Screening: 3-Layer Strategy Using GPT-3.5 and GPT-4 for Systematic Reviews

16 references·9 citing·40 second hop

this work references cited by second hop

References

2005Scoping studies: towards a methodological framework36,603 cited
2019Cochrane Handbook for Systematic Reviews of Interventions14,763 cited
2021PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews11,660 cited
2019Searching for and selecting studies643 cited
2019Best practice guidelines for abstract screening large‐evidence systematic reviews and meta‐analyses406 cited
2024Can large language models replace humans in systematic reviews? Evaluating GPT ‐4's efficacy in screening and extracting data from peer‐reviewed and grey literature in multiple languages266 cited
2020Error rates of human reviewers during abstract screening in systematic reviews214 cited
2020Single-reviewer abstract screening missed 13 percent of relevant studies: a crowd-based, randomized controlled trial206 cited
2023Automated Paper Screening for Clinical Reviews Using Large Language Models: Data Analysis Study205 cited
2018Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR)191 cited
2019Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error185 cited
2015Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers184 cited
2013Pinpointing needles in giant haystacks: use of text mining to reduce impractical screening workload in extremely large scoping reviews

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

References

Cited by