{
  "abstract": "ObjectiveTemperature readings are routinely stratified by race and ethnicity, though significant group differences may reflect physiology, instrument noise, or categorization rather than biology. This study tests whether these categories partition facial infrared thermography measurements beyond individual variation and baseline device noise.\n\nApproachA secondary analysis of thermography data (n = 951 adults) compares inner-canthus maximum (TCEmax) and full-face maximum (Tmax) temperatures across five administrative categories via one-way analysis of variance (ANOVA), mixed-effects modeling, Bland-Altman analysis against oral reference, and Monte Carlo sensitivity analysis.\n\nMain resultsANOVA returns significant group differences (p < 0.001; 2 = 0.04 for TCEmax, 0.03 for Tmax), but the total spread (0.2 {degrees}C to 0.3 {degrees}C) is comparable to device accuracy and within-subject variability. Individual variation accounts for 95% of TCEmax variance, and adding simulated Gaussian noise to participant-level temperatures ({sigma} = 0.10- 0.30 {degrees}C) preserves the complete group-mean rank order in as few as 37.5% of iterations.\n\nSignificanceTesting whether a group difference survives perturbation at the scale of instrument accuracy offers a check against treating significance alone as evidence of physiology. Here, observed group differences are small relative to measurement variability, and their physiological interpretation remains uncertain, with implications for clinical tools that treat administrative categories as biologically meaningful.",
  "abstract_shown": true,
  "affiliations": [
    "Yale University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "A. Lohier"
    },
    {
      "name": "A. G. Campbell"
    },
    {
      "name": "D. Chartash"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bbmhfb8jmjy5v.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.19.26365925",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.19.26365925v1.full",
  "id": "bbmhfb8jmjy5v",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.19.26365925v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2741|Infrared Thermography in Medicine"
    ]
  ],
  "openalex": "W7223087934",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.19.26365925v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bbmhfb8jmjy5v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:22Z",
      "native_id": "10.64898/2026.10.19.26365925",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.19.26365925v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Normative Temperature, Demographic Categories, and the Limits of Infrared Thermometry",
  "title_text": "Normative Temperature, Demographic Categories, and the Limits of Infrared Thermometry",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:22Z",
  "url": "https://research.draconitic.com/paper/bbmhfb8jmjy5v",
  "venue": "medRxiv",
  "versions": []
}