ReviewDiagnostics (Basel, Switzerland)2019
Current Diabetes Technology: Striving for the Artificial Pancreas.
Review in Diagnostics (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 63 citations in OpenAlex.
- Hydrogel-based approaches to target hypersensitivity mechanisms underlying autoimmune disease.Advanced drug delivery reviews · 2024Review
- Review
- Levothyroxine personalized treatment: is it still a dream?Frontiers in endocrinology · 2023Article
- Adolescents' and their parents' experiences of using a closed-loop system to manage type 1 diabetes in everyday life: qualitative study.Chronic illness · 2022Article
- Microneedle Patches Loaded with Nanovesicles for Glucose Transporter-Mediated Insulin Delivery.ACS nano · 2022Article
- Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions.Sensors (Basel, Switzerland) · 2022Article
- Review
- SPINA Carb: a simple mathematical model supporting fast in-vivo estimation of insulin sensitivity and beta cell function.Scientific reports · 2022Article
- Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives.Journal of diabetes science and technology · 2021Review
- A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems.Sensors (Basel, Switzerland) · 2021Article
- Fear of hypoglycemia, a game changer during physical activity in type 1 diabetes mellitus patients.World journal of diabetes · 2021Review
- The diabetes quality of life brief clinical inventory in combination with the management strategies in type 1 diabetes mellitus with or without the use of insulin pump.Diabetology international · 2021Article
- Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only.Sensors (Basel, Switzerland) · 2021Article
- Time in range-A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study.BMJ open diabetes research & care · 2021Article
- Neural Physiological Model: A Simple Module for Blood Glucose Prediction.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
- Blood glucose regulation and control of insulin and glucagon infusion using single model predictive control for type 1 diabetes mellitus.IET systems biology · 2020Article
- What Training, Support, and Resourcing Do Health Professionals Need to Support People Using a Closed-Loop System? A Qualitative Interview Study with Health Professionals Involved in the Closed Loop from Onset in Type 1 Diabetes (CLOuD) Trial.Diabetes technology & therapeutics · 2020Article
- Evolution of Insulin Delivery Devices: From Syringes, Pens, and Pumps to DIY Artificial Pancreas.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2020Review
- Article
- Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs).NPJ digital medicine · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes technology has continually evolved over the years to improve quality of life and ease of care for affected patients. Frequent blood glucose (BG) checks and multiple daily insulin injections have become standard of care in Type 1 diabetes (T1DM) management. Continuous glucose monitors (CGM) allow patients to observe and discern trends in their glycemic control. These devices improve quality of life for parents and caregivers with preset alerts for hypoglycemia. Insulin pumps have continued to improve and innovate since their emergence into the market. Hybrid closed-loop systems have harnessed the data gathered with CGM use to aid in basal insulin dosing and hypoglycemia prevention. As technology continues to progress, patients will likely have to enter less and less information into their pump system manually. In the future, we will likely see a system that requires no manual patient input and allows users to eat throughout the day without counting carbohydrates or entering in any blood sugars. As technology continues to advance, endocrinologists and diabetes providers need to stay current to better guide their patients in optimal use of emerging management tools.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.