Evidence mapPaperPMID 33793578Full record

ArticlePloS one2021

Interpreting blood GLUcose data with R package iglu.

Steven Broll, Jacek Urbanek, David Buchanan, Elizabeth Chun, John Muschelli, Naresh M Punjabi, Irina Gaynanova

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing 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

55 citing papers in PubMed.

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  17. Imputation of Missing Continuous Glucose Monitor Data.Journal of diabetes science and technology · 2026
    Article
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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

7 authors.

Steven BrollDepartment of Statistics, Texas A&M University, College Station, TX, United States of America.
Jacek UrbanekSchool of Medicine, Johns Hopkins University, Baltimore, MD, United States of America.
David BuchananDepartment of Statistics, Texas A&M University, College Station, TX, United States of America.
Elizabeth ChunDepartment of Biology, Texas A&M University, College Station, TX, United States of America.
John MuschelliJohns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America.
Naresh M PunjabiSchool of Medicine, Johns Hopkins University, Baltimore, MD, United States of America.
Irina GaynanovaDepartment of Statistics, Texas A&M University, College Station, TX, United States of America.ORCID 0000-0002-4116-0268

Funding

NHLBI NIH HHS R01 HL146709
6 · The paper itself

Abstract

Continuous Glucose Monitoring (CGM) data play an increasing role in clinical practice as they provide detailed quantification of blood glucose levels during the entire 24-hour period. The R package iglu implements a wide range of CGM-derived metrics for measuring glucose control and glucose variability. The package also allows one to visualize CGM data using time-series and lasagna plots. A distinct advantage of iglu is that it comes with a point-and-click graphical user interface (GUI) which makes the package widely accessible to users regardless of their programming experience. Thus, the open-source and easy to use iglu package will help advance CGM research and CGM data analyses. R package iglu is publicly available on CRAN and at https://github.com/irinagain/iglu.

Indexed as

SoftwareBlood GlucoseBlood Glucose Self-MonitoringData AnalysisDiabetes MellitusDisease ManagementHumansBlood Glucose

Identifiers

PMID33793578
PMCPMC8016265

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.