$\texttt{AMEND++}$: Benchmarking Eligibility Criteria Amendments in Clinical Trials
This work addresses the issue of frequent and costly amendments in clinical trials for researchers and administrators, but it is incremental as it builds on existing NLP and machine learning techniques.
The paper tackles the problem of predicting future amendments to clinical trial eligibility criteria, which cause delays and increased costs, by introducing a new NLP task and a benchmark suite AMEND++ with two datasets, and shows that their proposed CAMLM method consistently improves prediction performance.
Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly amended component. We introduce \textit{eligibility criteria amendment prediction}, a novel NLP task that aims to forecast whether the eligibility criteria of an initial trial protocol will undergo future amendments. To support this task, we release $\texttt{AMEND++}$, a benchmark suite comprising two datasets: $\texttt{AMEND}$, which captures eligibility-criteria version histories and amendment labels from public clinical trials, and $\verb|AMEND_LLM|$, a refined subset curated using an LLM-based denoising pipeline to isolate substantive changes. We further propose $\textit{Change-Aware Masked Language Modeling}$ (CAMLM), a revision-aware pretraining strategy that leverages historical edits to learn amendment-sensitive representations. Experiments across diverse baselines show that CAMLM consistently improves amendment prediction, enabling more robust and cost-effective clinical trial design.