{
  "abstract": "BackgroundSystem One Model is designed for structured decision-making and can provide probabilistic outputs, but their performance in domain-specific physical therapy tasks has not been established.\n\nObjectiveTo evaluate the performance and potential utility of Jev, a System One Model, for professional knowledge-based selection tasks in physical therapy by comparing it with GPT-4o.\n\nMethodsA total of 380 multiple-choice questions from the 2024 and 2025 Korean Physical Therapist Licensing Examinations were independently submitted to Jev and GPT-4o. The outcomes included answer accuracy, subgroup performance, output validity, API response latency, token usage, estimated API cost, and Jevs prediction uncertainty. A retrospective probability-based model-cascading simulation was also performed.\n\nResultsJev correctly answered 282 questions (74.2%), whereas GPT-4o answered 326 (85.8%), a difference of -11.6 percentage points. Jev produced valid structured responses for all questions, whereas GPT-4o produced three invalid responses. Jevs selected-answer probabilities discriminated correct from incorrect answers (AUROC = 0.866), and accuracy reached 99.3% when probabilities were greater than or equal to 0.90. The median API response latency was 0.94 s for Jev and 1.06 s for GPT-4o, with estimated costs of US$0.008 and US$0.161, respectively. At a retrospective cascading threshold of 0.70, accuracy was 86.6% with 175 simulated GPT-4o calls.\n\nConclusionJev showed lower overall accuracy than GPT-4o but provided reliable structured outputs, informative prediction probabilities, lower latency, and lower estimated cost. Its probabilistic outputs warrant further prospective evaluation for selective model escalation and decision-support workflows in physical therapy.",
  "abstract_shown": true,
  "affiliations": [
    "Yonsei University - Mirae Campus"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "J.-h. Kim"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fxmg0vzfnpzth.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.26.26364067",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.26.26364067v1.full",
  "id": "fxmg0vzfnpzth",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.26.26364067v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "rehabilitation medicine and physical therapy"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7214620558",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.26.26364067v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fxmg0vzfnpzth.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:35Z",
      "native_id": "10.64898/2026.09.26.26364067",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.26.26364067v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Comparative Evaluation of a System One Model and a General-Purpose Large Language Model on the Korean Physical Therapist Licensing Examination",
  "title_text": "Comparative Evaluation of a System One Model and a General-Purpose Large Language Model on the Korean Physical Therapist Licensing Examination",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:35Z",
  "url": "https://research.draconitic.com/paper/fxmg0vzfnpzth",
  "venue": "medRxiv",
  "versions": []
}