{
  "abstract": "This study aimed to evaluate the interpretive and applicative performance of three conditions applied to the formal EU-US, ESSKA-AOSSM-AASPT consensus on meniscus rehabilitation: (1) single-pass prompting, (2) sequential chain-of-thought prompting and (3) human expert-written output. This exploratory comparative study evaluated three conditions applied to two parts of the formal EU-US ESSKA-AOSSM-AASPT meniscus rehabilitation consensus: (1) single-pass prompting, (2) sequential chain-of-thought prompting using ChatGPT (GPT-5.1), Claude (Sonnet 4.5), and Gemini (3.0), and (3) human expert-written output from three collaborating human experts. For each part, three tasks were assessed: generation of summaries, application of summaries to three predefined clinical scenarios, and resistance to three predefined misinterpretation queries. Three blinded senior sports physiotherapists evaluated summary quality using five QUEST categories and rated the clinical application and misinterpretation resistance responses. Median ratings were compared using Friedman and post-hoc Wilcoxon signed-rank tests. Gwet's AC2 coefficient with quadratic weights was used to assess inter-rater agreement for each QUEST category and for the clinical application and misinterpretation resistance items. Across consensus parts, no statistically significant differences were identified between large language model prompting strategies and human expert outputs for QUEST categories, clinical application, and misinterpretation resistance queries. Sequential chain-of-thought prompting received higher numerical reviewer ratings than single-pass prompting for ChatGPT and Gemini, particularly for quality of information and trust and confidence, although differences were not statistically significant. Inter-rater agreement ranged from substantial to almost perfect (Gwet's AC2 = 0.79-0.99). No statistically significant differences in reviewer ratings were observed between ChatGPT, Claude, Gemini and human expert output, with generally high ratings for summaries and responses to clinical scenarios. Large language models may therefore represent a potential adjunct for summarisation and interpretation of sports medicine consensus recommendations to inform rehabilitation decision-making. Level V.",
  "abstract_shown": true,
  "affiliations": [
    "Unit of Physiotherapy, Department of Health and Rehabilitation Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden.",
    "Sahlgrenska Sports Medicine Center, Sahlgrenska Academy Gothenburg Sweden.",
    "Department of Orthopedic Surgery Balgrist University Hospital, University of Zürich Zurich Switzerland.",
    "Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg Sweden.",
    "Division of Occupational Therapy and Physical Therapy Cincinnati Children's Hospital and University of Cincinnati College of Medicine Cincinnati Ohio USA.",
    "Department of Physical Therapy University of Delaware Newark Delaware USA.",
    "Institute of Physiotherapy of Saint-Etienne - Saint-Michel Campus Saint- Etienne France.",
    "Center of Orthopaedics and Traumatology, University Hospital Brandenburg, Brandenburg Medical School Theodor Fontane Brandenburg/Havel Germany.",
    "Physiotherapy Research Laboratory University Centre of Physiotherapy and Rehabilitation, Faculty of Physiotherapy, Wroclaw Medical University Wroclaw Poland.",
    "Department of Orthopaedics Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg Gothenburg Sweden."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Jakob Lindskog",
      "orcid": "0009-0005-9146-0073"
    },
    {
      "affs": [
        2
      ],
      "name": "Felix Öttl",
      "orcid": "0000-0001-9721-685X"
    },
    {
      "affs": [
        3
      ],
      "name": "Yinan Yu",
      "orcid": "0000-0002-3221-7517"
    },
    {
      "affs": [
        4
      ],
      "name": "Mark V Paterno",
      "orcid": "0000-0003-2997-5023"
    },
    {
      "affs": [
        5
      ],
      "name": "Airelle Giordano",
      "orcid": "0009-0002-4357-3899"
    },
    {
      "affs": [
        6
      ],
      "name": "Alexandre Rambaud",
      "orcid": "0000-0001-7194-7777"
    },
    {
      "affs": [
        7
      ],
      "name": "Robert Prill",
      "orcid": "0000-0002-4916-1206"
    },
    {
      "affs": [
        8
      ],
      "name": "Aleksandra Królikowska",
      "orcid": "0000-0002-6283-5500"
    },
    {
      "affs": [
        1,
        9
      ],
      "name": "Kristian Samuelsson",
      "orcid": "0000-0001-5383-3370"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Eric Hamrin Senorski",
      "orcid": "0000-0002-9340-0147"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/469zesdjprbjp.bib",
  "cited_by": 0,
  "doi": "10.1002/jeo2.70964",
  "fulltext_url": "https://europepmc.org/articles/PMC13715498",
  "id": "469zesdjprbjp",
  "issns": [
    "2197-1153"
  ],
  "landing_url": "https://europepmc.org/article/MED/42953767",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Paper"
    ],
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      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7224133550",
  "pdf_url": "https://europepmc.org/articles/PMC13715498?pdf=render",
  "pmcid": "PMC13715498",
  "pmid": "42953767",
  "published": "2026-10-28",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/469zesdjprbjp.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:40Z",
      "native_id": "MED:42953767",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42953767"
    },
    {
      "first_seen": "2026-11-01T12:22:47Z",
      "native_id": "PMC13715498",
      "source": "pmc",
      "url": "https://doi.org/10.1002/jeo2.70964"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "No significant differences between large language model prompting strategies and human experts in summarisation of 'the formal EU-US meniscus rehabilitation consensus: An ESSKA-AOSSM-AASPT Initiative'.",
  "title_text": "No significant differences between large language model prompting strategies and human experts in summarisation of 'the formal EU-US meniscus rehabilitation consensus: An ESSKA-AOSSM-AASPT Initiative'.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:22:47Z",
  "url": "https://research.draconitic.com/paper/469zesdjprbjp",
  "venue": "Journal of experimental orthopaedics",
  "versions": []
}