{
  "abstract": "Objective: Trofinetide, approved in 2023 as the first Rett syndrome treatment, lacks scalable real-world monitoring because chart review is labor-intensive. We developed a dual large language model (LLM) consensus pipeline to extract 18 treatment-response phenotypes, comprising 4 adverse effects, 13 Rett clinical features, and medication refusal, from clinical notes. Materials and Methods: We analyzed 26,763 notes from 108 patients with confirmed sustained trofinetide exposure at Texas Children's Hospital. The pipeline filters notes by keyword, classifies each through two LLMs requiring consensus, and aggregates a patient-by-timepoint matrix. Architecture and prompt were selected on a diarrhea development set, then frozen and applied to the other 17 phenotypes with the phenotype definition substituted, as an out-of-sample test. The reported prompt comparison scores 183 expert-labeled cells. Results: A prompt requiring diarrhea to be active and severe performed best (84.2 $\\pm$ 0.0% accuracy; F1 = 0.674), 47 percentage points above regular-expression matching ($p < 0.0001$). Expert reviewers judged six of the pipeline's calls per phenotype, three positive and three negative, and agreed with 90.2% (92/102). The 13 Rett clinical features are manifestations of the disorder itself rather than adverse effects of trofinetide. Gross and fine motor impairment, language/communication, and stereotypy were documented present in 92-100% of patients at every visit. Discussion: In-context LLM extraction captured multi-domain treatment responses. The outcome is documented presence at each visit, not a direction-of-change judgment. Conclusion: Coupling in-context LLM reasoning with a consensus architecture offers a scalable alternative to keyword and regular-expression extraction for real-world evidence, adapting to new phenotypes and medications through configuration alone.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.22062",
  "author_total": 9,
  "authors": [
    {
      "name": "Daniel Palacios"
    },
    {
      "name": "Guantong Qi"
    },
    {
      "name": "Mikael Guzman Karlsson"
    },
    {
      "name": "Shawntel K. David"
    },
    {
      "name": "Mohammed Sameeruddin"
    },
    {
      "name": "Bernhard Suter"
    },
    {
      "name": "Zhandong Liu"
    },
    {
      "name": "Davut Pehlivan"
    },
    {
      "name": "Hu Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0fyg8jhrvzf9h.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.22062",
  "fulltext_url": null,
  "id": "0fyg8jhrvzf9h",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.22062",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CL"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1311|Genetics and Neurodevelopmental Disorders"
    ]
  ],
  "openalex": "W7222727639",
  "pdf_url": "https://arxiv.org/pdf/2610.22062",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0fyg8jhrvzf9h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:55Z",
      "native_id": "2610.22062",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.22062"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Documenting Trofinetide Treatment Phenotypes in Rett Syndrome with a Dual-LLM Consensus Pipeline",
  "title_text": "Documenting Trofinetide Treatment Phenotypes in Rett Syndrome with a Dual-LLM Consensus Pipeline",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:55Z",
  "url": "https://research.draconitic.com/paper/0fyg8jhrvzf9h",
  "venue": "arXiv",
  "versions": []
}