{
  "abstract": "ObjectivesTo quantify complementary meanings of binary clinical calculator results: information beyond the observed outcome frequency, post-result risk, information direction, and precision.\n\nDesignCatalogue-based cross-sectional analysis of published aggregate performance data, with a reproducible row-level audit.\n\nSettingThe 847-calculator MDCalc catalogue and a fixed collection of publicly available reports assembled without a systematic literature search.\n\nParticipantsEvaluations comparing a binary calculator result with a binary clinical outcome using a complete or reconstructable 2 x 2 table.\n\nMain outcome measuresPercentage reduction in uncertainty and corresponding information gain in bits; risk after positive and negative classifications; the proportion of information from each classification; and whether the upper 95% confidence bound for post-negative risk supported specified thresholds.\n\nResultsThe analysis included 482 evaluations of 407 calculators. The median study size was 422 and the median observed outcome frequency was 17.6%. Results reduced uncertainty by a median 14.9% (interquartile range 6.2%-30.5%; 95% confidence interval 12.1% to 17.2%), corresponding to 0.093 bits. In 329 evaluations (68.3%), one classification supplied more than 60% of average information. PERC reduced uncertainty by 4.3% on average while its negative classification lowered observed risk from 7.6% to 1.0%. Two HEART thresholds in the same cohort shifted the positive-classification information share from 39.1% to 79.8%. Post-negative risk was below 2% in 157 evaluations by point estimate, but the upper 95% confidence bound was below 2% in only 67; 90 of 157 (57%) did not support the apparent threshold.\n\nConclusionsClinical calculator results have no single quantitative meaning. Conventional performance, information added beyond the observed outcome frequency, post-result risk, classification-specific information, and precision provide complementary interpretations. Whether acting on a result improves care remains a separate question.\n\nKey messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIClinical calculators are conventionally described using sensitivity, specificity, predictive values, likelihood ratios, discrimination, and calibration, but these measures answer different questions about a result. C_LI\n\nWhat this study addsO_LIAcross 482 evaluations, calculator results removed a median 14.9% of starting uncertainty; one classification supplied more than 60% of average information in 329 evaluations. C_LIO_LIOf 157 evaluations with a post-negative point estimate below 2%, only 67 had an upper 95% confidence bound below 2%. C_LI\n\nHow this study might affect research, practice or policyO_LIReports of clinical calculators could pair conventional accuracy with starting risk, post-result risks and intervals, and, when average learning matters, uncertainty reduction and its direction; none alone establishes clinical benefit. C_LI",
  "abstract_shown": true,
  "affiliations": [
    "Division of AI, Department of Emergency Medicine, Massachusetts General Hospital, Mass General Brigham, Boston, Massachusetts, USA"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 14,
  "authors": [
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      "name": "S. He"
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      "name": "J. W. Joseph"
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      "name": "P. Safari"
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      "name": "N. Akomeh"
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    {
      "name": "A. Goff"
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    {
      "name": "P. J. Slusarz"
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    {
      "name": "A. Mohamed"
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    {
      "name": "S. Lord"
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    {
      "name": "J. N. Goldstein"
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    {
      "name": "A. S. Raja"
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    {
      "name": "C. Kabrhel"
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    {
      "name": "B. W. Locke"
    },
    {
      "name": "C. Rohlfsen"
    },
    {
      "name": "D. M. Liebovitz"
    }
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  "bibtex": "https://research.draconitic.com/paper/1mgeh8s0bkb4j.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.08.26362556",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26362556v1.full",
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  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
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    [
      "openalex",
      "2613|Statistical Methods in Epidemiology"
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  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26362556v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1mgeh8s0bkb4j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:19Z",
      "native_id": "10.64898/2026.09.08.26362556",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.08.26362556v1"
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  "subjects": [
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      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
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  "title": "Meaning of clinical calculator results: a cross-sectional analysis of the MDCalc catalogue",
  "title_text": "Meaning of clinical calculator results: a cross-sectional analysis of the MDCalc catalogue",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:19Z",
  "url": "https://research.draconitic.com/paper/1mgeh8s0bkb4j",
  "venue": "medRxiv",
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}