Evidence map›Paper›PMID 34109837›Full record

ArticleJournal of diabetes science and technology2022

Examining Sensor Agreement in Neural Network Blood Glucose Prediction.

Aaron P Tucker, Arthur G Erdman, Pamela J Schreiner, Sisi Ma, Lisa S Chow

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Aaron P TuckerEarl E. Bakken Medical Devices Center, University of Minnesota, Minneapolis, MN, USA.ORCID 0000-0002-1720-9543
Arthur G ErdmanEarl E. Bakken Medical Devices Center, University of Minnesota, Minneapolis, MN, USA.
Pamela J SchreinerDivision of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA.
Sisi MaDivision of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA.
Lisa S ChowDivision of Diabetes, Endocrinology and Metabolism, University of Minnesota, Minneapolis, MN, USA.ORCID 0000-0002-6210-2307

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022
$34.9M
NCATS NIH HHS UL1 TR002494
6 · The paper itself

Abstract

Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting (

Indexed as

Blood GlucoseDiabetes Mellitus, Type 2AlgorithmsBlood Glucose Self-MonitoringFemaleHumansMaleNeural Networks, ComputerBlood Glucoseblood glucose predictioncontinuous glucose monitoringneural network

Identifiers

PMID34109837
PMCPMC9631521

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.