Evidence map›Paper›PMID 39101163›Full record

ArticleDiabetology international2024

A sensor-augmented pump with a predictive low-glucose suspend system could lead to an optimal time in target range during pregnancy in Japanese women with type 1 diabetes.

Rie Kaneshima Tamura, Noriko Kodani, Arata Itoh, Shu Meguro, Hiroshi Kajio, Hiroshi Itoh

Abstract read
In one paragraph

Article in Diabetology international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Rie Kaneshima TamuraDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi Shinjuku-Ku, Tokyo, 160-8582 Japan.
Noriko KodaniDepartment of Diabetes, Endocrinology and Metabolism, Center Hospital of the National Center for Global Health and Medicine, 1-21-1 Toyama Shinjuku-Ku, Tokyo, 162-8655 Japan.ORCID 0000-0002-4443-1545
Arata ItohDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi Shinjuku-Ku, Tokyo, 160-8582 Japan.
Shu MeguroDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi Shinjuku-Ku, Tokyo, 160-8582 Japan.
Hiroshi KajioDepartment of Diabetes, Endocrinology and Metabolism, Center Hospital of the National Center for Global Health and Medicine, 1-21-1 Toyama Shinjuku-Ku, Tokyo, 162-8655 Japan.
Hiroshi ItohDivision of Nephrology, Endocrinology and Metabolism, Department of Internal Medicine, Keio University School of Medicine, 35 Shinanomachi Shinjuku-Ku, Tokyo, 160-8582 Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: It is challenging for pregnant women with type 1 diabetes to maintain optimum glucose level to attain good neonatal outcomes. This study evaluated the efficacy of sensor-augmented insulin pump (SAP) with a predictive low-glucose suspend (PLGS) system in pregnant Japanese women with type 1 diabetes. Materials and methods: SAP with PLGS was used in 11 of the 22 women with type 1 diabetes who delivered between 2011 and 2021 at the two medical institutions in Japan. Glucose management, insulin delivery suspension time (IST) and neonatal outcomes were retrospectively studied. Results: In SAP with PLGS cases ( Conclusion: SAP with PLGS was safely and effectively used in pregnant women with type 1 diabetes to achieve target glucose levels without increasing the risk of hypoglycemia, which may have led to good neonatal outcomes. Supplementary Information: The online version contains supplementary material available at 10.1007/s13340-024-00716-7.

Indexed as

Predictive low-glucose suspend systemPregnancySensor-augmented pumpTime in rangeType 1 diabetes

Identifiers

PMID39101163
PMCPMC11291783

What OpenQuestion holds

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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.