@inproceedings{alharbi-stevenson-2026-nlp,
    title = "Can {NLP} Models Detect When One Publication Outweighs Twenty? Predicting Systematic Review Conclusion Changes",
    author = "Alharbi, Ebrahim  and
      Stevenson, Mark",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.68/",
    pages = "843--852",
    ISBN = "979-8-89176-434-7",
    abstract = "Systematic reviews underpin evidence-based medicine but can outdate quickly when new evidence appears. We formulate a novel prediction task: given a review and new studies that have appeared since its publication, predict whether the review{'}s conclusions will change. A dataset of 3,326 Cochrane review-update pairs is constructed and a range of approaches explored including feature-based baselines, zero and few-shot LLMs, in addition to parameter efficient fine-tuning. Fine-tuning Qwen2.5 14B achieves the highest AUC-ROC (70.4{\%})."
}