The content of biomedical papers predicts future clinical trials: a proof-of-concept framework tested on CAR-T cell therapy
- 1University of Guelph
Abstract
This paper shows that it is possible to predict which newly published biomedical research papers will be connected to subsequent clinical trials. We use CAR-T cell therapy as a proof-of-concept setting because its development can be followed from published research through clinical testing, regulatory approval, and commercialization. We reconstructed connections between research papers and clinical trials and trained machine-learning models to predict which papers were associated with research that progressed to clinical testing.
Using 65 paper-inherent indicators, including scientific topic, language, biomedical orientation, and team structure, the models achieved a cross-validated AUC of 0.908. The resulting rankings were also practically informative. In a retrospective analysis using no information generated after 2016, 80% of the top 20 papers came from laboratories associated with one of 13 predefined major CAR-T clinical or commercial outcomes.
As an additional finding, first-year citations alone achieved an AUC of 0.797, while provenance indicators based on author, journal, and institutional reputation performed less strongly and added little to the paper-inherent model.
Our results show that whether biomedical research papers will be connected to subsequent clinical trials can be predicted from newly published papers with high discrimination in CAR-T. Our findings could help research organizations and pharmaceutical companies identify papers, laboratories, and therapeutic programs that warrant closer evaluation before clinical outcomes are known.
Related
- Analytic Interoperability Framework for High-Fidelity Representation of Perioperative Electronic Health Record Data in the OMOP Common Data Model Using Clinical Representation and Analytic Concept Reconstruction
- A multi-agent large language model framework for structured clinical interviewing and psychiatric screening: a proof-of-concept
- Effect Estimates, Effect Sizes, Confidence Intervals, and P-values: A Framework for Reporting and Interpreting Biomedical Research
- Biofilm Formation on Invasive Medical Devices in Adult Intensive Care Units: A Scoping Review of Biomedical and Clinical Evidence
- Evaluating synthetic-data fidelity in two-group biomedical studies: a multidimensional validation framework
BibTeX
@misc{brennan2026content,
title = {{The content of biomedical papers predicts future clinical trials: a proof-of-concept framework tested on CAR-T cell therapy}},
author = {J. Brennan and J. Slater and Y. Yan},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.10.26365160},
url = {https://www.medrxiv.org/content/10.64898/2026.10.10.26365160v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - The content of biomedical papers predicts future clinical trials: a proof-of-concept framework tested on CAR-T cell therapy
AU - J. Brennan
AU - J. Slater
AU - Y. Yan
PY - 2026
DA - 2026/10/10
PB - medRxiv
DO - 10.64898/2026.10.10.26365160
UR - https://www.medrxiv.org/content/10.64898/2026.10.10.26365160v1
AB - This paper shows that it is possible to predict which newly published biomedical research papers will be connected to subsequent clinical trials. We use CAR-T cell therapy as a proof-of-concept setting because its development can be followed from published research through clinical testing, regulatory approval, and commercialization. We reconstructed connections between research papers and clinical trials and trained machine-learning models to predict which papers were associated with research that progressed to clinical testing. Using 65 paper-inherent indicators, including scientific topic, language, biomedical orientation, and team structure, the models achieved a cross-validated AUC of 0.908. The resulting rankings were also practically informative. In a retrospective analysis using no information generated after 2016, 80% of the top 20 papers came from laboratories associated with one of 13 predefined major CAR-T clinical or commercial outcomes. As an additional finding, first-year citations alone achieved an AUC of 0.797, while provenance indicators based on author, journal, and institutional reputation performed less strongly and added little to the paper-inherent model. Our results show that whether biomedical research papers will be connected to subsequent clinical trials can be predicted from newly published papers with high discrimination in CAR-T. Our findings could help research organizations and pharmaceutical companies identify papers, laboratories, and therapeutic programs that warrant closer evaluation before clinical outcomes are known.
ER -