Model Releases
exttt{AMEND++}: Benchmarking Eligibility Criteria Amendments in Clinical Trials
arXiv:2601.06300v2 Announce Type: replace Abstract: Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly
arXiv:2601.06300v2 Announce Type: replace Abstract: Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly amended component. We introduce extit{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 exttt{AMEND++}, a benchmark suite comprising two datasets: exttt{AMEND}, which captures eligibility-criteria version histories and amendment labels from public clinical trials, and erb|AMEND_LLM|, a refined subset curated using an LLM-based denoising pipeline to isolate substantive changes. We further propose extit{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.
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Source: arXiv cs.CL | 2026-07-30