Evidence map›Paper›PMID 37942014›Full record

ArticleiScience2023

Personalized glucose-lowering effect of chiglitazar in type 2 diabetes.

Qi Huang, Xiantong Zou, Yingli Chen, Leili Gao, Xiaoling Cai, Lingli Zhou, Fei Gao, Jian Zhou, Weiping Jia, Linong Ji

Open access · goldAbstract read
In one paragraph

Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 27% 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

7 citing papers in PubMed, 5 citations in OpenAlex.

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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

10 authors at 3 institutions in 1 country.

Qi HuangDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Xiantong ZouDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Yingli ChenDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Leili GaoDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Xiaoling CaiDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Lingli ZhouDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Fei GaoDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai 200233, China.
Jian ZhouDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai 200233, China.
Weiping JiaDepartment of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai 200233, China.
Linong JiDepartment of Endocrinology and Metabolism, Peking University People's Hospital, Beijing 100044, China.
Peking University · CNPeking University People's Hospital · CNShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chiglitazar (carfloglitazar) is a peroxisome proliferator-activated receptor pan-agonist presenting non-inferior glucose-lowering efficacy with sitagliptin in patients with type 2 diabetes. To delineate the subgroup of patients with greater benefit from chiglitazar, we conducted a machine learning-based post-hoc analysis in two randomized controlled trials. We established a character phenomap based on 13 variables and estimated HbA

Indexed as

Clinical endocrinologyEndocrinologyHuman metabolismMachine learning

Identifiers

PMID37942014
PMCPMC10628820
OpenAlexW4387581096

What OpenQuestion 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 OpenQuestion graph.