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Scientific Evidence & Sources

This file documents the scientific sources used to shape IINTS-AF defaults, validation targets, and report framing.

Scope: - Pre-clinical simulation and retrospective forecasting only. - Not a treatment recommendation engine. - Not a medical device.

Use iints sources to print the same source manifest from the packaged SDK.

How Sources Map to SDK Components

SDK area Why it exists Source IDs
Validation targets (TIR, TBR, TAR, hypoglycemia framing) Keep benchmark interpretation aligned with accepted diabetes metrics ada_2026_glycemic_goals, attd_2019_time_in_range
CGM + AID context in docs/reports Keep language aligned with current standards for technology use ada_2026_diabetes_technology
AID benchmark envelopes Compare algorithm behavior against realistic closed-loop outcomes nejm_2019_control_iq, adapt_2022_ahcl
Meal timing / pre-bolus scenarios Avoid unrealistic meal-response assumptions cobry_2010_meal_bolus_timing
Insulin action profiles (rapid and ultra-rapid) Parameterize onset/peak assumptions from pharmacology literature heise_2017_fiasp_pkpd, klaff_2020_urli_pkpd
Input validation and CGM lag rationale Keep signal handling biologically plausible wentholt_2004_cgm_lag
Virtual patient meal dynamics Ground meal disturbance dynamics in established models dalla_man_2007_meal_model
Simulator realism/validation framing Align in-silico model evaluation with accepted simulator literature visentin_2018_uvapadova
Exercise stress scenarios Use consensus guidance for exercise-related glucose behavior riddell_2017_exercise_consensus
Forecast training data provenance Use publicly documented dataset references marling_2020_ohiot1dm

Source List

  1. ada_2026_glycemic_goals
    ADA Professional Practice Committee. Glycemic Goals and Hypoglycemia: Standards of Care in Diabetes—2026.
    DOI: 10.2337/dc26-S006

  2. ada_2026_diabetes_technology
    ADA Professional Practice Committee. Diabetes Technology: Standards of Care in Diabetes—2026.
    DOI: 10.2337/dc26-S007

  3. attd_2019_time_in_range
    Battelino T, Danne T, Bergenstal RM, et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation. Diabetes Care. 2019.
    DOI: 10.2337/dci19-0028

  4. nejm_2019_control_iq
    Brown SA, et al. Six-Month Randomized, Multicenter Trial of Closed-Loop Control in Type 1 Diabetes. N Engl J Med. 2019.
    DOI: 10.1056/NEJMoa1907863

  5. adapt_2022_ahcl
    Benhamou PY, et al. Advanced hybrid closed loop therapy versus conventional treatment in adults with type 1 diabetes (ADAPT). Lancet Diabetes Endocrinol. 2022.
    DOI: 10.1016/S2213-8587(22)00212-1

  6. cobry_2010_meal_bolus_timing
    Cobry E, et al. Timing of Meal Insulin Boluses to Achieve Optimal Postprandial Glycemic Control. J Diabetes Sci Technol. 2010.
    DOI: 10.1177/193229681000400404

  7. heise_2017_fiasp_pkpd
    Heise T, et al. A Faster-Onset Formulation of Insulin Aspart. Clin Pharmacokinet. 2017.
    DOI: 10.1007/s40262-017-0510-8

  8. klaff_2020_urli_pkpd
    Klaff LJ, et al. Ultra Rapid Lispro Demonstrates Accelerated Pharmacokinetics and Pharmacodynamics. Diabetes Obes Metab. 2020.
    DOI: 10.1111/dom.14049

  9. wentholt_2004_cgm_lag
    Wentholt IME, et al. How glucose sensors can facilitate therapy in diabetes management. Diabetes Technol Ther. 2004.
    DOI: 10.1089/dia.2004.6.615

  10. dalla_man_2007_meal_model
    Dalla Man C, Rizza RA, Cobelli C. Meal simulation model of the glucose-insulin system. IEEE Trans Biomed Eng. 2007.
    DOI: 10.1109/TBME.2007.893506

  11. visentin_2018_uvapadova
    Visentin R, et al. The University of Virginia/Padova Type 1 Diabetes Simulator Matches the 2014 DMMS.R. J Diabetes Sci Technol. 2018.
    DOI: 10.1177/1932296818757747

  12. riddell_2017_exercise_consensus
    Riddell MC, et al. Exercise management in type 1 diabetes: a consensus statement. Lancet Diabetes Endocrinol. 2017.
    DOI: 10.1016/S2213-8587(17)30014-1

  13. marling_2020_ohiot1dm
    Marling C, Bunescu R. The OhioT1DM Dataset for Blood Glucose Level Prediction: Update 2020. CEUR Workshop Proceedings.
    Paper: ceur-ws.org/Vol-2675/paper2.pdf

Reproducibility Note

For report reproducibility, pair this file with: - run metadata (run_metadata.json) - manifest hashes (run_manifest.json) - dataset lineage fields in research outputs (source_file_sha256, split metadata).