Evidence mapPaperPMID 37658243Full record

SynthesisEndocrine2024

Anti-CD3 monoclonal antibodies in treatment of type 1 diabetes: a systematic review and meta-analysis.

Yuting Liu, Weixia Li, Yu Chen, Xin Wang

Abstract readMeta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Endocrine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
3.1field-weighted citation impact, top 8% 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. 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

4 authors at 2 institutions in 1 country.

Yuting LiuDepartment of Endocrinology, Jiangsu Province Hospital of Traditional Chinese Medicine/the Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.
Weixia LiThe First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China.
Yu ChenDepartment of Endocrinology, Jiangsu Province Hospital of Traditional Chinese Medicine/the Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China.
Xin WangDepartment of Endocrinology, Jiangsu Province Hospital of Traditional Chinese Medicine/the Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China. xin.wang@njucm.edu.cn.
Jiangsu Province Hospital · CNNanjing University of Chinese Medicine · CN

Funding

Jiangsu Province Hospital of Chinese Medicine k2018yrc19
6 · The paper itself

Abstract

purposeThis meta-analysis aimed to assess the efficacy and safety of anti-CD3 monoclonal antibodies (mAbs) for type 1 diabetes.

methodsWe searched PubMed, Embase and Cochrane until 23 February 2023 for randomized controlled trials that compared anti-CD3 mAbs with placebo in type 1 diabetes. The primary outcome was the area under the curve (AUC) of C-peptide, daily insulin dose or HbA

resultsTotally 12 trials that included 1870 participants were eligible for inclusion in the review. Compared with the control group, anti-CD3 mAbs increased AUC of C-peptide at 1 year (P = 0.0005, MD 0.14, 95% CI [0.06, 0.22], I

conclusionsOur results suggest that anti-CD3 mAbs were a potential therapy for improving AUC of C-peptide and insulin use in type 1 diabetes.

Indexed as

Diabetes Mellitus, Type 1Antibodies, MonoclonalAntibodies, Monoclonal, HumanizedC-PeptideHumansInsulinAntibodies, MonoclonalAntibodies, Monoclonal, HumanizedC-PeptideInsulinAnti-CD3ImmunotherapyMeta-analysisOtelixizumabTeplizumabType 1 diabetes

Identifiers

PMID37658243
OpenAlexW4386365603

What Socratic 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 Socratic graph.