{
  "abstract": "Introduction: Continuous glucose monitoring (CGM) produces rich time series data. Standard metrics such as time-in-range summarize glycemia but miss the sequence of changes. We aimed to characterize glucose dynamics using empirical transition probability techniques. Materials and Methods: Using publicly available data for 244 participants who enrolled in the Insulin-Only Bionic Pancreas (IOBP2) randomized clinical trial, we mapped each CGM reading to one of five states: < 54, 54–69, 70–180, 181–250, or >250 mg/dL. We counted state changes between consecutive readings collected every 5 minutes. For each participant, we built a 5 × 5 transition probability matrix and averaged matrices within comparator groups. Statistical inference using estimated transition probability matrices was compared to inferences based on standard metrics including mean glucose, standard deviation, coefficient of variation, time-in-range metrics, Glycemia Risk Index (GRI), Mean of Daily Differences (MODD), and Mean Amplitude of Glycemic Excursions (MAGE). Results: Transition probability analysis showed more frequent moves from hyperglycemia (181–250 or >250 mg/dL) to the in-range (70–180 mg/dL) state for the bionic pancreas that was evaluated in the IOBP2 trial compared to control. This pattern appeared in both adults and in children and persisted through the trial. Conventional CGM metrics also showed treatment differences in mean glucose, GRI, and time in range; MODD, MAGE, and coefficient of variation showed less consistent differences between treatment groups. Conclusion: Transition probability matrices provide an informative summary of glucose dynamics that complements established CGM metrics. In IOBP2, this approach highlighted improved patterns of recovery from hyperglycemia with the bionic pancreas compared with control. Future studies should determine whether transition-based summaries are associated with clinically meaningful outcomes.",
  "abstract_shown": true,
  "affiliations": [
    "Northwestern University Feinberg School of Medicine, Department of Preventive Medicine, Division of Biostatistics and Informatics, Chicago, Illinois, United States of America",
    "Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut, United States of America"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
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        0
      ],
      "name": "Yuanzhi Yu"
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    {
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        1
      ],
      "name": "Margaret Banker"
    },
    {
      "name": "Jennifer Sherr"
    },
    {
      "name": "Denise M. Scholtens"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/10vnnkrxq48n6.bib",
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  "doi": "10.1371/journal.pone.0358361",
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    "1932-6203"
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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      "2712|Diabetes Management and Research"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358361&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/10vnnkrxq48n6.ris",
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    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0358361",
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      "url": "https://doi.org/10.1371/journal.pone.0358361"
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      "origin": "plos:endocrinology",
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  "title": "Empirical transition probability modeling of continuous glucose monitoring data",
  "title_text": "Empirical transition probability modeling of continuous glucose monitoring data",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/10vnnkrxq48n6",
  "venue": "PLOS One",
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