Evidence mapPaperPMID 37424694Full record

ArticleThe Lancet regional health. Western Pacific2023

Device-supported automated basal insulin titration in adults with type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials.

Yingying Luo, Yaping Chang, Zhan Zhao, Jun Xia, Chenchen Xu, Yong Mong Bee, Xiaoying Li, Wayne H-H Sheu, Margaret McGill, Siew Pheng Chan and 12 more

Open access · goldAbstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 16% of its field
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Ideal automation for insulin management - with interpretation of risk ratio.The Lancet regional health. Western Pacific · 2023
    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

22 authors at 19 institutions in 13 countries.

Yingying LuoDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Yaping ChangDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario L8S 4L8, Canada.
Zhan ZhaoTianjin Tiantian Biotechnology Co., Ltd., Tianjin 300000, China.
Jun XiaNottingham Ningbo GRADE Centre, University of Nottingham Ningbo China, Ningbo, Zhejiang 315100, China.
Chenchen XuTianjin Tiantian Biotechnology Co., Ltd., Tianjin 300000, China.
Yong Mong BeeDepartment of Endocrinology, Singapore General Hospital, Singapore 169608, Singapore.
Xiaoying LiDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Wayne H-H SheuDivision of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei 222, Taiwan.
Margaret McGillDiabetes Centre, Royal Prince Alfred Hospital, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales 2050, Australia.
Siew Pheng ChanDepartment of Medicine, Faculty of Medicine, University of Malaya, Lembah Pantai, Kuala Lumpur 59100, Malaysia.
Marisa DeodatMichael G. DeGroote Cochrane Canada and McMaster GRADE Centres, McMaster University, Hamilton, Ontario L8V 5C2, Canada.
Ketut SuastikaDivision of Endocrinology and Metabolism, Department of Internal Medicine, Faculty of Medicine, Prof. IGNG Ngoerah Hospital, Udayana University, Denpasar, Bali 80114, Indonesia.
Khue Nguyen ThyHo Chi Minh University of Medicine and Pharmacy Medic Medical Center, Ho Chi Minh City 700000, Vietnam.
Liming ChenChu Hsien-I Memorial (Metabolic Diseases) Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin 300134, China.
Alice Pik Shan KongDivision of Endocrinology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong Special Administrative Region 999077, China.
Wei ChenDepartment of Clinical Nutrition, Department of Health Medicine, Chinese Academy of Medical Sciences-Peking Union Medical College, Peking Union Medical College Hospital, Beijing 100730, China.
Chaicharn DeerochanawongCollege of Medicine, Rangsit University, Bangkok 10400, Thailand.
Daisuke YabeDepartments of Diabetes, Endocrinology and Metabolism/Rheumatology and Clinical Immunology, Gifu University Graduate School of Medicine, Gifu 501-1194, Japan.
Weigang ZhaoDepartment of Endocrinology, Peking Union Medical College Hospital, Beijing 100730, China.
Soo LimDepartment of Internal Medicine, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam 13620, South Korea.
Xiaomei YaoCenter for Clinical Practice Guideline Conduction and Evaluation, Children's Hospital of Fudan University, Shanghai 201100, China.
Linong JiDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNPeking University · CNTianjin Tianhe Hospital · CNChildren's Hospital of Fudan University · CNChinese University of Hong Kong · HKGifu University · JPImpact · CAMcMaster University · CARangsit University · THRoyal Prince Alfred Hospital · AUSeoul National University Bundang Hospital · KRSingapore General Hospital · SGSun Yat-sen University · CNTaipei Veterans General Hospital · TWTianjin Infectious Diseases Hospital · CNUdayana University · IDUniversity Malaya Medical Centre · MYUniversity of Medicine and Pharmacy at Ho Chi Minh City · VNUniversity of Nottingham Ningbo China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Technological advances make it possible to use device-supported, automated algorithms to aid basal insulin (BI) dosing titration in patients with type 2 diabetes. Methods: A systematic review and meta-analysis of randomized controlled trials were performed to evaluate the efficacy, safety, and quality of life of automated BI titration versus conventional care. The literature in Medline, Embase, Web of Science, and the Cochrane databases from January 2000 to February 2022 were searched to identify relevant studies. Risk ratios (RRs), mean differences (MDs), and their 95% confidence intervals (CIs) were calculated using random-effect meta-analyses. Certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. Findings: Six of the 7 eligible studies (889 patients) were included in meta-analyses. Low- to moderate-quality evidence suggests that patients who use automated BI titration versus conventional care may have a higher probability of reaching a target of HbA Interpretation: Automated BI titration is associated with small benefits in reducing HbA Funding: Sponsored by the Chinese Geriatric Endocrine Society.

Indexed as

Automated titrationBasal insulinGlucose controlHbA1c levelHypoglycemiaSystematic reviewType 2 diabetes

Identifiers

PMID37424694
PMCPMC10326709
OpenAlexW4360875817

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.