Article

Development of meta-prompts for Large Language Models to screen titles and abstracts for diagnostic test accuracy reviews

2023-11-01

Abstract excerpt

Systematic reviews (SRs) are a critical component of evidence-based medicine, but the process of screening titles and abstracts is time-consuming. This study aimed to develop and externally validate a method using large language models to classify abstracts for diagnostic test accuracy (DTA) systematic reviews, thereby reducing the human workload. We used a previously collected dataset for developing DTA abstract...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 56517dce-90f5-5bfc-9113-9e074a262f7d
DOI: 10.1101/2023.10.31.23297818

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Development of meta-prompts for Large Language Models to screen titles and abstracts for diagnostic test accuracy reviewsDOI 10.1101/2023.10.31.23297818Read

Sensitivity, specificity and avoidable workload of using a large language models for title and abstract screening in systematic reviews and meta-analysesSemantic proximity Read

Validation of Generative AI Models to Expedite Title and Abstract Screening in Systematic ReviewsSemantic proximity Read

RAPID: Reliable and efficient Automatic generation of submission rePorting checklists with Large language moDelsSemantic proximity Read

Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studiesSemantic proximity Read

Will generative AI help solve systematic literature reviews? Evidence from a 2-year research programmeSemantic proximity Read

End-to-End Reliability of Automated Systems for Diagnostic Data Extraction: A Benchmark Study in Uro-Oncologic Evidence SynthesisSemantic proximity Read

Using GPT-4 to Automate the Generation of Lay Summaries for Cancer PublicationsSemantic proximity Read

Evaluating Eight Retrieval-Augmented Generation (RAG) Large Language Models’ Responses to Clinical Questions: A Comparative StudySemantic proximity Read

Collaborative Large Language Models for Automated Data Extraction in Living Systematic ReviewsSemantic proximity Read

High Consistency, Limited Accuracy: Evaluating Large Language Models for Binary Medical DiagnosisSemantic proximity Read

Large Language Model-Assisted Data Extraction in Systematic Reviews: A Case Study of Automated Comparative Performance EvaluationSemantic proximity Read

A statistical framework for evaluating the repeatability and reproducibility of large language modelsSemantic proximity Read