$\texttt{AMEND++}$: Benchmarking Eligibility Criteria Amendments in Clinical Trials
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915719792295936 |
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| author | Das, Trisha Beigi, Mandis Aptekar, Jacob Sun, Jimeng |
| author_facet | Das, Trisha Beigi, Mandis Aptekar, Jacob Sun, Jimeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06300 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | $\texttt{AMEND++}$: Benchmarking Eligibility Criteria Amendments in Clinical Trials Das, Trisha Beigi, Mandis Aptekar, Jacob Sun, Jimeng Computation and Language Artificial Intelligence Machine Learning 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. |
| title | $\texttt{AMEND++}$: Benchmarking Eligibility Criteria Amendments in Clinical Trials |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2601.06300 |