{
  "abstract": "This paper shows that it is possible to predict which newly published biomedical research papers will be connected to subsequent clinical trials. We use CAR-T cell therapy as a proof-of-concept setting because its development can be followed from published research through clinical testing, regulatory approval, and commercialization. We reconstructed connections between research papers and clinical trials and trained machine-learning models to predict which papers were associated with research that progressed to clinical testing.\n\nUsing 65 paper-inherent indicators, including scientific topic, language, biomedical orientation, and team structure, the models achieved a cross-validated AUC of 0.908. The resulting rankings were also practically informative. In a retrospective analysis using no information generated after 2016, 80% of the top 20 papers came from laboratories associated with one of 13 predefined major CAR-T clinical or commercial outcomes.\n\nAs an additional finding, first-year citations alone achieved an AUC of 0.797, while provenance indicators based on author, journal, and institutional reputation performed less strongly and added little to the paper-inherent model.\n\nOur results show that whether biomedical research papers will be connected to subsequent clinical trials can be predicted from newly published papers with high discrimination in CAR-T. Our findings could help research organizations and pharmaceutical companies identify papers, laboratories, and therapeutic programs that warrant closer evaluation before clinical outcomes are known.",
  "abstract_shown": true,
  "affiliations": [
    "University of Guelph"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "J. Brennan"
    },
    {
      "name": "J. Slater"
    },
    {
      "name": "Y. Yan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ea1g0tcphj172.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.10.26365160",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.10.26365160v1.full",
  "id": "ea1g0tcphj172",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.10.26365160v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "1804|scientometrics and bibliometrics research"
    ]
  ],
  "openalex": "W7221566168",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.10.26365160v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ea1g0tcphj172.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:43Z",
      "native_id": "10.64898/2026.10.10.26365160",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.10.26365160v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "The content of biomedical papers predicts future clinical trials: a proof-of-concept framework tested on CAR-T cell therapy",
  "title_text": "The content of biomedical papers predicts future clinical trials: a proof-of-concept framework tested on CAR-T cell therapy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:43Z",
  "url": "https://research.draconitic.com/paper/ea1g0tcphj172",
  "venue": "medRxiv",
  "versions": []
}