Method for automatic adjustment of an insulin bolus calculator: In silico robustness evaluation under intra-day variability
Open accessAbstract
Background and objective Insulin bolus calculators are simple decision support software tools incorporated in most commercially available insulin pumps and some capillary blood glucose meters. Although their clinical benefit has been demonstrated, their utilisation has not been widespread and their performance remains suboptimal, mainly because of their lack of flexibility and adaptability. One of the difficulties that people with diabetes, clinicians and carers face when using bolus calculators is having to set parameters and adjust them on a regular basis according to changes in insulin requirements. In this work, we propose a novel method that aims to automatically adjust the parameters of a bolus calculator. Periodic usage of a continuous glucose monitoring device is required for this purpose.Methods To test the proposed method, an in silico evaluation under real-life conditions was carried out using the FDA-accepted Type 1 diabetes mellitus (T1DM) UVa/Padova simulator. Since the T1DM simulator does not incorporate intra-subject variability and uncertainty, a set of modifications were introduced to emulate them. Ten adult and ten adolescent virtual subjects were assessed over a 3-month scenario with realistic meal variability. The glycaemic metrics: mean blood glucose; percentage time in target; percentage time in hypoglycaemia; risk index, low blood glucose index; and blood glucose standard deviation, were employed for evaluation purposes. A t-test statistical analysis was carried out to evaluate the benefit of the presented algorithm against a bolus calculator without automatic adjustment.Results The proposed method statistically improved (p<0.05) all glycemic metrics evaluating hypoglycaemia on both virtual cohorts: percentage time in hypoglycaemia (i.e. BG<70 mg/dl) (adults: 2.7±4.0 vs. 0.4±0.7, p=0.03; adolescents: 7.1±7.4 vs. 1.3±2.4, p=0.02) and low blood glucose index (LBGI) (adults: 1.1±1.3 vs. 0.3±0.2, p=0.002; adolescents: 2.0±2.19 vs. 0.7±1.4, p=0.05). A statistically significant improvement was also observed on the blood glucose standard deviation (BG SD mg/dL) (adults: 33.5±13.7 vs. 29.2±8.3, p=0.01; adolescents: 63.7±22.7 vs. 44.9±23.9, p=0.01). Apart from a small increase in mean blood glucose on the adult cohort (129.9±11.9 vs. 133.9±11.6, p=0.03), the rest of the evaluated metrics, despite showing an improvement trend, did not experience a statistically significant change.Conclusions A novel method for automatically adjusting the parameters of a bolus calculator has the potential to improve glycemic control in T1DM diabetes management.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Herrero et al. proposed an approach to automatically adjust carbohydrate-to-insulin ratio and correction factor using CGM measurements, updating parameter values daily in a run-to-run fashion.
“Herrero et al. [41] proposed an approach to automatically adjust CR and CF using CGM measurements. This method updates the parameters values every day in a run-to-run (R2R) fashion.”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [41]
Results from a simulated experiment suggested that run-to-run offers a promising approach to optimizing standard formula parameters.
“The results obtained from a simulated experiment suggested that R2R offers a promising approach to optimizing SF parameters [41].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [41]
Cites
(1993) · New England Journal of Medicine · doi:10.1056/nejm199309303291401 · Cited by 24,667 · see citing works
Meal Simulation Model of the Glucose-Insulin System
Dalla Man, C., Rizza, R. A., & Cobelli, C. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.893506 · Cited by 955 · see citing works
<i>In Silico</i> Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes
Kovatchev, B. P., Breton, M., Dalla Man, C., & Cobelli, C. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300106 · Cited by 760 · see citing works
Outpatient Glycemic Control with a Bionic Pancreas in Type 1 Diabetes
Russell, S. J., El-Khatib, F. H., Sinha, M., Magyar, K. L., McKeon, K., Goergen, L. G., BALLIRO, C. A., HILLARD, M. A., Nathan, D. M., & Damiano, E. R. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1314474 · Cited by 605 · see citing works
Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes
Brazeau, A., Mircescu, H., Desjardins, K., Leroux, C., Strychar, I. M., Ékoé, J.-M., & RABASA-LHORET, R. P. R. (2012) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2012.10.024 · Cited by 266 · see citing works
Continuous glucose monitoring systems for type 1 diabetes mellitus
Langendam, M., Luijf, Y. M., Hooft, L., DeVries, J. H., Mudde, A. H., & Scholten, R. J. (2012) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd008101.pub2 · Cited by 262 · see citing works
&NA; (1994) · The Endocrinologist · doi:10.1097/00019616-199403000-00024 · Cited by 207 · see citing works
Schiffrin, A., & Belmonte, M. M. (1982) · Diabetes Care · doi:10.2337/diacare.5.5.479 · Cited by 200 · see citing works
Schmidt, S., & Nørgaard, K. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814532906 · Cited by 170 · see citing works
Bolus Calculator: A Review of Four “Smart” Insulin Pumps
Zisser, H., Robinson, L., Bevier, W. C., Dassau, E., Ellingsen, C., Doyle, F. J., & Jovanovic, L. G. (2008) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2007.0284 · Cited by 164 · see citing works
Gross, T. M., Kayne, D. M., King, A. B., Rother, C., & Juth, S. E. (2003) · Diabetes Technology & Therapeutics · doi:10.1089/152091503765691848 · Cited by 144 · see citing works
(2016) · Wiley eBooks · doi:10.1002/9781118924853 · Cited by 137 · see citing works
Kowalski, A. J. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2009.0031 · Cited by 127 · see citing works
Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial
Magni, L., Forgione, M., Toffanin, C., Man, C. D., Kovatchev, B. P., Nicolao, G. D., & Cobelli, C. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300512 · Cited by 106 · see citing works
Scheiner, G., & Boyer, B. A. (2004) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2004.11.005 · Cited by 104 · see citing works
Guidelines for Optimal Bolus Calculator Settings in Adults
Walsh, J. S. A., Roberts, R., & Bailey, T. S. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500118 · Cited by 100 · see citing works
Shashaj, B., Busetto, E., & Sulli, N. (2008) · Diabetic Medicine · doi:10.1111/j.1464-5491.2008.02549.x · Cited by 96 · see citing works
Barnard, K. D., Parkin, C. G., Young, A. J., & Ashraf, M. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600117 · Cited by 93 · see citing works
Advanced Insulin Bolus Advisor based on Run-To-Run Control and Case-Based Reasoning
Herrero, P., Pesl, P., Reddy, M., Oliver, N. S., Georgiou, P., & Toumazou, C. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2331896 · Cited by 90 · see citing works
Self-citationSussman, A., Taylor, E. J., Patel, M., Ward, J., Alva, S., Lawrence, A., & Ng, R. H. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600218 · Cited by 82 · see citing works
Freckmann, G., Baumstark, A., Schmid, C., Pleus, S., Link, M., & Haug, C. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0208 · Cited by 76 · see citing works
Prandial Insulin Dosing Using Run-to-Run Control
Palerm, C. C., Zisser, H., Bevier, W. C., Jovanovic, L. G., & Doyle, F. J. (2007) · Diabetes Care · doi:10.2337/dc06-2115 · Cited by 74 · see citing works
Haidar, A., Elleri, D., Kumareswaran, K., Leelarathna, L., Allen, J. M., Caldwell, K., Murphy, H. R., Wilinska, M. E., Acerini, C. L., Evans, M. L., et al. (2013) · Diabetes Care · doi:10.2337/dc13-0485 · Cited by 53 · see citing works
Barnard, K. D., Hood, K. K., Weissberg-Benchell, J. A., Aldred, C., Oliver, N. S., & Laffel, L. M. (2014) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2014.0305 · Cited by 48 · see citing works
Self-citationGarg, S. K., Bookout, T., McFann, K., Kelly, W. C., Beatson, C. R., Ellis, S. L., Gutin, R. S., & Gottlieb, P. A. (2008) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2007.0303 · Cited by 43 · see citing works
Coordinated Basal—Bolus Infusion for Tighter Postprandial Glucose Control in Insulin Pump Therapy
Bondía, J., Dassau, E., Zisser, H., Calm, R., Vehı́, J., Jovanovic, L. G., & Doyle, F. J. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300110 · Cited by 41 · see citing works
Zisser, H., Wagner, R. S., Pleus, S., Haug, C., Jendrike, N. R. M., Parkin, C., Schweitzer, M. A., & Freckmann, G. (2010) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2010.0064 · Cited by 41 · see citing works
Can a Tool That Automates Insulin Titration Be a Key to Diabetes Management?
Bergenstal, R. M., Bashan, E., McShane, M., Johnson, M. L., & Hodish, I. (2012) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0303 · Cited by 38 · see citing works
Lepore, G., Dodesini, A. R., Nosari, I., Scaranna, C., Corsi, A., & Trevisan, R. (2012) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2012.04.001 · Cited by 34 · see citing works
The Current Status of Bolus Calculator Decision-Support Software
Klonof, D. C. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600501 · Cited by 32 · see citing works
Fuzzy-Logic-Based Supervisor of Insulin Bolus Delivery for Patients with Type 1 Diabetes Mellitus
Liu, S.-W., Huang, H., Lin, C.-H., & Chien, I. (2013) · Industrial & Engineering Chemistry Research · doi:10.1021/ie301621u · Cited by 14 · see citing works
Use of Automated Bolus Calculators for Diabetes Management
Schwartz, F., & Marling, C. R. (2010) · touchREVIEWS in Endocrinology · doi:10.17925/ee.2013.09.02.92 · Cited by 10 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day
Visentin, R., Campos-Náñez, E., Schiavon, M., Lv, D., Vettoretti, M., Breton, M., Kovatchev, B. P., Dalla Man, C., & Cobelli, C. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818757747 · Cited by 280 · see citing works
Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment
Cappon, G., Acciaroli, G., Vettoretti, M., Facchinetti, A., & Sparacino, G. (2017) · Electronics · doi:10.3390/electronics6030065 · Cited by 242 · see citing works
Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors
Vettoretti, M., Cappon, G., Facchinetti, A., & Sparacino, G. (2020) · Sensors · doi:10.3390/s20143870 · Cited by 176 · see citing works
Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation
Zhu, T., Li, K., Herrero, P., & Georgiou, P. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3014556 · Cited by 119 · see citing works
Self-citationToward a Run-to-Run Adaptive Artificial Pancreas: In Silico Results
Toffanin, C., VISENTIN, R., Messori, M., Palma, F. D., Magni, L., & Cobelli, C. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2652062 · Cited by 107 · see citing works
Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An >In Silico Validation
Zhu, T., Li, K., Herrero, P., & Georgiou, P. (2021) · UCL Discovery (University College London) · Cited by 79 · see citing works
Self-citationAn Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning
Zhu, T., Li, K., Kuang, L., Herrero, P., & Georgiou, P. (2020) · Sensors · doi:10.3390/s20185058 · Cited by 78 · see citing works
Self-citationReddy, M., Pesl, P., Xenou, M., Toumazou, C., Johnston, D., Georgiou, P., Herrero, P., & Oliver, N. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0413 · Cited by 70 · see citing works
A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning
Sun, Q., Jankovic, M. V., Budzinski, J., Moore, B. L., Diem, P., Stettler, C., & Mougiakakou, S. G. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2887067 · Cited by 67 · see citing works
Model individualization for artificial pancreas
Messori, M., Toffanin, C., Favero, S. D., Nicolao, G. D., Cobelli, C., & Magni, L. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.06.006 · Cited by 68 · see citing works
Herrero, P., Bondia, J., Adewuyi, O., Pesl, P., El-Sharkawy, M., Reddy, M., Toumazou, C., Oliver, N., & Georgiou, P. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.05.010 · Cited by 63 · see citing works
Self-citationAn Advanced Bolus Calculator for Type 1 Diabetes: System Architecture and Usability Results
Pesl, P., Herrero, P., Reddy, M., Xenou, M., Oliver, N. S., Johnston, D. G., Toumazou, C., & Georgiou, P. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2464088 · Cited by 59 · see citing works
Self-citationPersonalized Adaptive CBR Bolus Recommender System for Type 1 Diabetes
Torrent‐Fontbona, F., & López, B. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2813424 · Cited by 52 · see citing works
Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility
Cobelli, C., & Kovatchev, B. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231195081 · Cited by 46 · see citing works
Case-Based Reasoning for Insulin Bolus Advice
Pesl, P., Herrero, P., Reddy, M., Oliver, N. S., Johnston, D. G., Toumazou, C., & Georgiou, P. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816629986 · Cited by 38 · see citing works
Self-citationAvari, P., Leal, Y., Herrero, P., Wos, M., Jugnee, N., Arnoriaga‐Rodríguez, M., Thomas, M., Liu, C., Massana, Q., López, B., et al. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0301 · Cited by 37 · see citing works
Self-citationAutomatic adaptation of basal therapy for Type 1 diabetic patients: A Run-to-Run approach
Toffanin, C., Messori, M., Cobelli, C., & Magni, L. (2016) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2016.09.002 · Cited by 36 · see citing works
Bolus Advisors: Sources of Error, Targets for Improvement
Walsh, J. S. A., Roberts, R., Bailey, T. S., & Heinemann, L. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817718213 · Cited by 27 · see citing works
Jafar, A., Fathi, A. E., & Haidar, A. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.105936 · Cited by 25 · see citing works
Cappon, G., Facchinetti, A., Sparacino, G., Georgiou, P., & Herrero, P. (2019) · Sensors · doi:10.3390/s19143168 · Cited by 23 · see citing works
Self-citationAutomatic Adaptation of Basal Insulin Using Sensor-Augmented Pump Therapy
Herrero, P., Bondía, J., Giménez, M., Oliver, N. S., & Georgiou, P. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818761752 · Cited by 22 · see citing works
Self-citationSchiavon, M., Man, C. D., & Cobelli, C. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0248 · Cited by 22 · see citing works
Fathi, A. E., Kearney, R., Palisaitis, E., Boulet, B., & Haidar, A. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3023555 · Cited by 20 · see citing works
Control-Oriented Model With Intra-Patient Variations for an Artificial Pancreas
Moscoso-Vásquez, M., Colmegna, P. H., Rosales, N., Garelli, F., & Sánchez‐Peña, R. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2969389 · Cited by 20 · see citing works
Torrent‐Fontbona, F., Massana, J., & López, B. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.12.036 · Cited by 18 · see citing works
Sun, Q., Jankovic, M. V., & Mougiakakou, S. G. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857178 · Cited by 17 · see citing works
Adaptive basal insulin recommender system based on Kalman filter for type 1 diabetes
Torrent‐Fontbona, F. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.02.015 · Cited by 17 · see citing works
Open-loop glucose control: Automatic IOB-based super-bolus feature for commercial insulin pumps
Rosales, N., Battista, H. D., Vehı́, J., & Garelli, F. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.03.007 · Cited by 15 · see citing works
Advanced carbohydrate counting: An engineering perspective
Reiterer, F., & Freckmann, G. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.06.003 · Cited by 16 · see citing works
Kalita, D., Sharma, H., Panda, J. K., & Mirza, K. B. (2024) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2024.104241 · Cited by 16 · see citing works
Intra-patient dynamic variations in Type 1 Diabetes: A review
Moscoso-Vásquez, M., Colmegna, P. H., & Sánchez‐Peña, R. (2016) · The proceedings of the IEEE Conference on Control Applications/The proceedings of the ... IEEE Conference on Control Applications · doi:10.1109/cca.2016.7587866 · Cited by 13 · see citing works
Unsworth, R., Armiger, R., Jugnee, N., Thomas, M., Herrero, P., Georgiou, P., Oliver, N. S., & Reddy, M. (2023) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0504 · Cited by 13 · see citing works
Self-citationCai, D., Wu, W., Cescon, M., Liu, W., Ji, L., & Shi, D. (2023) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2023.100897 · Cited by 14 · see citing works
Jafar, A., Pasqua, M., Olson, B., & Haidar, A. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102749 · Cited by 11 · see citing works
An artificial pancreas based on simple control algorithms and physiological insight
Jørgensen, J. B., Boiroux, D., & Mahmoudi, Z. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.06.196 · Cited by 11 · see citing works
A CBR-based bolus recommender system for type 1 diabetes
Torrent‐Fontbona, F., López, B., & Pozo-Alonso, A. (2017) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 4 · see citing works
Sun, Q., Jankovic, M. V., & Mougiakakou, S. G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08586 · Cited by 4 · see citing works
Modelado y simulación de tecnologías para el tratamiento de la diabetes
Rosales, N. (2020) · doi:10.35537/10915/97299 · Cited by 2 · see citing works
Continuous Glucose Monitoring and Bolus Calculators: A Good Combination
Heinemann, L., & Thomas, A. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231204371 · Cited by 2 · see citing works
Chen, J., Shi, D., Cai, D., Liu, W., Ji, L., & Shi, L. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3554905 · Cited by 1 · see citing works
Artificial Intelligence and Its Application in Insulin Bolus Calculators
Mansour, A., Amroun, K., & Habbas, Z. (2020) · Springer eBooks · doi:10.1007/978-3-030-57552-6_5
Lu, X., Cai, D., Zhang, W., Liang, P., & Shi, D. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3632880
Pancreas and pancreatic hormones: Insulin dosing behavior and “insulin metrics”
Xue, J., & Chang, X. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-30263-3.00006-3
- Authors
- Herrero, P., Pesl, P., Bondia, J., Reddy, M., Oliver, N., Georgiou, P., & Toumazou, C.
- Affiliations
- Imperial College London (GB)
- Imperial College Healthcare NHS Trust (GB)
- Corresponding author
- Herrero, Pau
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2015-04
- Volume
- 119
- Issue
- 1
- Pages
- 1-8
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2083543575
- Cited by
- 43
- References
- 32
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 5 · 2021: 2 · 2020: 8 · 2019: 5 · 2018: 8 · 2017: 5 · 2016: 5 · 2015: 1
- FWCI
- 5.51
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Funders
- Wellcome Trust
- National Institute for Health Research
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Keywords
- auto-tuning, type 1 diabetes, continuous glucose monitoring, intraday variability, hypoglycemia, low blood glucose index, time in target, insulin pump, UVA/Padova simulator, in silico evaluation, adolescents, adults, glycemic control, decision support software
- MeSH terms
- Adolescent, Adult, Algorithms, Computer Simulation, Decision Support Techniques, Diabetes Mellitus, Type 1, Humans, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Software, User-Computer Interface, Blood Glucose Self-Monitoring, Computational Biology, Postprandial Period
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
