# textttAMEND++: Benchmarking Eligibility Criteria Amendments in Clinical Trials

arXiv: https://arxiv.org/abs/2601.06300 · HTML: https://mlsanity.com/paper/2601.06300/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Development](https://mlsanity.com/paper/2406.10292/) (2024)
- [Information Extraction of Clinical Trial Eligibility Criteria](https://mlsanity.com/paper/2006.07296/) (2020)
- [SECRET: Semi-supervised Clinical Trial Document Similarity Search](https://mlsanity.com/paper/2505.10780/) (2025)

## Abstract (arXiv)

Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly amended component. We introduce textiteligibility 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 textttAMEND++, a benchmark suite comprising two datasets: textttAMEND, which captures eligibility-criteria version histories and amendment labels from public clinical trials, and verb|AMENDLLM|, a refined subset curated using an LLM-based denoising pipeline to isolate substantive changes. We further propose textitChange-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.