{
  "abstract": "Background/Objectives: Large language models (LLMs) are consulted for clinical decision support, and their reliability is judged by hallucination prevalence-a measure treating every unsupported element as equivalent, although a fabricated citation and a fabricated protocol differ in what a clinician acting on them would do. We tested whether prevalence tracks clinical risk. Methods: Five LLMs answered a 100-item orthodontic benchmark validated by three external orthodontists (content validity index 0.923). The reference standard was fixed by construction for the 45 items naming a non-existent entity, so any substantive elaboration is unsupported by design; citations were adjudicated against PubMed and CrossRef. Responses were coded with a seven-category taxonomy and assigned to clinical, operational, or epistemic severity tiers in a post hoc exploratory stratification, pre-specified rather than prospectively registered. Because all models answered the same items, comparisons used Cochran's Q with pairwise McNemar tests and generalised estimating equations clustered on item. Results: Of 500 responses, 449 (89.8%) contained a hallucination but only 65 (13.0%; 95% CI 10.3-16.2) were clinically consequential: prevalence was roughly sevenfold greater than the rate of clinically consequential output as the authors defined it. Between-model differences were large for undifferentiated prevalence (73-100%; Q = 50.51, p < 0.001) and contracted at the clinical tier (10-16%; Q = 10.00, p = 0.040), where no pairwise contrast survived adjustment. Item-level clustering was far stronger for clinically consequential output than for undifferentiated prevalence (intra-class correlation 0.83 versus 0.07). Conclusions: Prevalence and clinically consequential error are not interchangeable and rank models differently. The stratification is exploratory and author-defined, and the benchmark stress-tests susceptibility to fabricated premises rather than surveying natural use.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Orthodontics, Faculty of Dentistry, Istanbul Health and Technology University, İmrahor Cad. No. 82, Sütlüce, Beyoğlu, 34275 Istanbul, Türkiye."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ferdi Allaf"
    },
    {
      "affs": [
        0
      ],
      "name": "Mustafa Ozcan",
      "orcid": "0009-0008-8331-9493"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ey1w56g8d8cet.bib",
  "cited_by": 0,
  "doi": "10.3390/diagnostics16193180",
  "fulltext_url": "https://europepmc.org/articles/PMC13664601",
  "id": "ey1w56g8d8cet",
  "issns": [
    "2075-4418"
  ],
  "landing_url": "https://europepmc.org/article/MED/42882334",
  "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": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7214903597",
  "pdf_url": "https://europepmc.org/articles/PMC13664601?pdf=render",
  "pmcid": "PMC13664601",
  "pmid": "42882334",
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ey1w56g8d8cet.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:16Z",
      "native_id": "MED:42882334",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42882334"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Severity Stratification Changes What Hallucination Rates Mean: An Item-Matched Audit of Five Large Language Models in Orthodontic Decision Support.",
  "title_text": "Severity Stratification Changes What Hallucination Rates Mean: An Item-Matched Audit of Five Large Language Models in Orthodontic Decision Support.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:16Z",
  "url": "https://research.draconitic.com/paper/ey1w56g8d8cet",
  "venue": "Diagnostics (Basel, Switzerland)",
  "versions": []
}