ArticleJournal of diabetes science and technology2015
GoCARB in the Context of an Artificial Pancreas.
Article in Journal of diabetes science and technology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02546063 (Carbohydrate Estimation Supported by the GoCARB System in Individuals With Type 1 Diabetes), which is not on this map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Carbohydrate Estimation Supported by the GoCARB System in Individuals With Type 1 Diabetes
Who cites it
7 citing papers in PubMed.
- Trial
- Revolutionizing diabetes care: the role of artificial intelligence in prevention, diagnosis, and patient care.Journal of diabetes and metabolic disorders · 2025Review
- Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions.Frontiers in endocrinology · 2022 · on this mapReview
- An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2020Article
- Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.Journal of medical Internet research · 2018Review
- The challenges of achieving postprandial glucose control using closed-loop systems in patients with type 1 diabetes.Diabetes, obesity & metabolism · 2018Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn an artificial pancreas (AP), the meals are either manually announced or detected and their size estimated from the blood glucose level. Both methods have limitations, which result in suboptimal postprandial glucose control. The GoCARB system is designed to provide the carbohydrate content of meals and is presented within the AP framework.
methodThe combined use of GoCARB with a control algorithm is assessed in a series of 12 computer simulations. The simulations are defined according to the type of the control (open or closed loop), the use or not-use of GoCARB and the diabetics' skills in carbohydrate estimation.
resultsFor bad estimators without GoCARB, the percentage of the time spent in target range (70-180 mg/dl) during the postprandial period is 22.5% and 66.2% for open and closed loop, respectively. When the GoCARB is used, the corresponding percentages are 99.7% and 99.8%. In case of open loop, the time spent in severe hypoglycemic events (<50 mg/dl) is 33.6% without the GoCARB and is reduced to 0.0% when the GoCARB is used. In case of closed loop, the corresponding percentage is 1.4% without the GoCARB and is reduced to 0.0% with the GoCARB.
conclusionThe use of GoCARB improves the control of postprandial response and glucose profiles especially in the case of open loop. However, the most efficient regulation is achieved by the combined use of the control algorithm and the GoCARB.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.