More signal versus more noise: comparing full text and abstract as inputs for large language model-based...

Large language models (LLMs) offer significant potential for automating clinical trial classification by eligibility criteria. However, the optimal input data remain unclear: while abstracts provide a condensed signal, full-text articles contain substantially more information. Whether this additional signal improves performance or whether accompanying noise negatively affects the model's reasoning capabilities remains unclear.

GPT-5 was applied to classify 200 randomized controlled oncology trials, labelling whether patients with localized and/or metastatic disease were eligible. Each trial was classified twice-using the abstract and full text-and outputs were compared with manually annotated ground-truth labels. Performance was assessed using accuracy, precision, recall, and F1 score, and statistical significance using the McNemar test.

For identifying trials including patients with localized disease, GPT-5 achieved an accuracy of 86% (95% CI, 81%-91%; F1 = 0.90) using abstracts and 92% (95% CI, 88%-95%; F1 = 0.94) using full texts (P = .027). Performance for detecting trials, which include patients with metastatic disease, was comparably high (99% vs 98% accuracy; F1 = 1.00-0.99). Overall accuracy for assigning combined labels increased from 86% (95% CI, 81%-91%) using abstracts to 92% (95% CI, 88%-95%) using full texts (P = .027).

Providing full-text articles to GPT-5 significantly improved the classification of oncology trials by eligibility criteria in this dataset. Full-text analysis appears particularly valuable for extracting eligibility criteria in oncology that are frequently omitted or not explicitly described within the abstract.