Monitoring Changes in Clinical Trial Primary Outcomes Using Large Language Models

This cross-sectional study investigates the prevalence of meaningful changes to the outcomes in preregistered clinical trials using large language models.

Authors

Institutions

Publication Details

Journal
JAMA Network Open
Published
2026-09-16
DOI
https://doi.org/10.1001/jamanetworkopen.2026.34214
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Monitoring Changes in Clinical Trial Primary Outcomes Using Large Language Models

Wonjin Yoon, Ian Bulovic, Susmitha Wunnava, Adam G. Dunn et al.
JAMA Network Open
Advanced Causal Inference Techniques
article

Monitoring Changes in Clinical Trial Primary Outcomes Using Large Language Models

Wonjin Yoon, Ian Bulovic, Susmitha Wunnava, Adam G. Dunn, Florence T. Bourgeois, Timothy Miller
article en

Abstract

This cross-sectional study investigates the prevalence of meaningful changes to the outcomes in preregistered clinical trials using large language models.

JAMA Network OpenVol. 9(9)
Boston Children's Hospital (US), The University of Sydney (AU), Harvard University (US)
Quality Education
Openalex Percentile: Top 7%
Advanced Causal Inference Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.