Evidence map›Paper›PMID 39158996›Full record

ArticleJournal of diabetes science and technology2024

Predicting Glucose Values: A New Era for Continuous Glucose Monitoring.

Bernhard Kulzer, Lutz Heinemann

Abstract readEditorial
In one paragraph

Article in Journal of diabetes science and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Algor-Ethics in Diabetes Care: Mapping the Route.Diabetes/metabolism research and reviews · 2026
    Review
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Bernhard KulzerResearch Institute Diabetes Academy Mergentheim, Bad Mergentheim, Germany.ORCID 0000-0001-9120-4479
Lutz HeinemannScience Consulting in Diabetes GmbH, Düsseldorf, Germany.ORCID 0000-0003-2493-1304

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The last 25 years of CGM have been characterized above all by providing better and more accurate glucose values in real time and analyzing the measured glucose values. Trend arrows are the only way to look into the future, but they are often too imprecise for therapy adjustment. While AID systems provide algorithms to use glucose values for glucose control, this has not been possible with stand-alone CGM systems, which are most used by people with diabetes. By analyzing the measured values with algorithms, often supported by AI, this should be possible in the future. This provides the user with important information about the further course of the glucose level, such as during the night. Predictive approaches can be used by next-generation CGM systems. These systems can proactively prevent glucose events such as hypo- or hyperglycemia. With the Accu-Chek® SmartGuide Predict app, an integral part of a novel CGM system, and the Glucose Predict (GP) feature, people with diabetes have the first commercially available CGM system with predictive algorithms. It characterizes the CGM systems of the future, which not only analyze past values and current glucose values in the future, but also use these values to predict future glucose progression.

Indexed as

AlgorithmsBlood GlucoseBlood Glucose Self-MonitoringContinuous Glucose MonitoringDiabetes MellitusDiabetes Mellitus, Type 1HumansPredictive Value of TestsBlood GlucoseCGMdiabetes self-managementfear of hypoglycemiaglucose excursionsglucose predictionhypoglycemia

Identifiers

PMID39158996
PMCPMC11418460

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.