{
  "abstract": "Determining ideal cut-off scores is a critical step in screening and diagnostic test evaluation. While the Area Under the Curve (AUC) with Receiver Operating Characteristic (ROC) curve are frequently reported, these do not directly indicate the best threshold for decision-making. In addition, pursuing an optimal solution might not always be practical. This paper presents a practical guideline for selecting cut-off scores based on AUC values in conjunction with sensitivity, specificity, and intended scientific use. The study outline recommended strategies for different AUC ranges, incorporating realistic considerations and scientific priorities. The framework offers flexibility, including when to prioritize sensitivity, specificity, or balanced performance via Youden's Index and other techniques. This guideline aims to support researchers and clinicians in making evidence-based, transparent, and context-sensitive decisions when interpreting screening or diagnostic accuracy results.",
  "abstract_shown": true,
  "affiliations": [
    "Clinical Research Centre, Institute For Clinical Research, National Institutes of Health Ministry of Health Malaysia, Sarawak General Hospital"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mohamad Adam Bujang",
      "orcid": "0000-0001-6231-4062"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8h02a9ygq475y.bib",
  "cited_by": 7,
  "doi": "10.3389/frma.2026.1808675",
  "fulltext_url": null,
  "id": "8h02a9ygq475y",
  "issns": [
    "2504-0537"
  ],
  "landing_url": "https://doi.org/10.3389/frma.2026.1808675",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2613|Statistical Methods in Epidemiology"
    ]
  ],
  "openalex": "W7163671810",
  "pdf_url": "https://www.frontiersin.org/journals/research-metrics-and-analytics/articles/10.3389/frma.2026.1808675/pdf",
  "pmcid": "PMC13284882",
  "pmid": "42338917",
  "published": "2026-06-05",
  "publisher": "Frontiers Media",
  "ris": "https://research.draconitic.com/paper/8h02a9ygq475y.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T13:20:39Z",
      "native_id": "W7163671810",
      "source": "openalex",
      "url": "https://doi.org/10.3389/frma.2026.1808675"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "openalex:2613|Statistical Methods in Epidemiology",
      "subfield": "statistics"
    }
  ],
  "title": "Guidelines for setting cut-off scores in AUC (AUC-GUIDE): balancing sensitivity, specificity, and purpose",
  "title_text": "Guidelines for setting cut-off scores in AUC (AUC-GUIDE): balancing sensitivity, specificity, and purpose",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T13:20:22Z",
  "url": "https://research.draconitic.com/paper/8h02a9ygq475y",
  "venue": "Frontiers in Research Metrics and Analytics",
  "versions": []
}