Evidence map›Paper›PMID 35360517›Full record

SynthesisBioMed research international2022

Diagnostic Value of Serum Chitinase-3-Like Protein 1 for Liver Fibrosis: A Meta-analysis.

Xiaoting Huang, Jialing Zhuang, Yongqiang Yang, Jiaxin Jian, Wen Ai, Chunyong Liu, Wenzhi Tang, Changyu Jiang, Yongshen He, Lesheng Huang and 1 more

Open access · hybridAbstract readMeta-Analysis
In one paragraph

Synthesis in BioMed research international, 2022. 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
2.5field-weighted citation impact, top 11% 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, 13 citations in OpenAlex.

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

11 authors at 3 institutions in 1 country.

Xiaoting HuangMedical Research Center, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518051, China.ORCID https://orcid.org/0000-0001-9831-8815
Jialing ZhuangDepartment of Laboratory Medicine, Guangdong Provincial Hospital of Chinese Medicine, Zhuhai 519015, China.ORCID https://orcid.org/0000-0002-5700-1691
Yongqiang YangDepartment of Laboratory Medicine, Guangdong Provincial Hospital of Chinese Medicine, Zhuhai 519015, China.ORCID https://orcid.org/0000-0001-5071-4691
Jiaxin JianDepartment of Laboratory Medicine, Guangdong Provincial Hospital of Chinese Medicine, Zhuhai 519015, China.ORCID https://orcid.org/0000-0003-4134-4865
Wen AiMedical Research Center, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518051, China.ORCID https://orcid.org/0000-0002-4213-0036
Chunyong LiuDepartment of Basic Medicine, Zhaoqing Medical College, Zhaoqing 526020, China.ORCID https://orcid.org/0000-0001-8838-777X
Wenzhi TangDepartment of Laboratory Medicine, Guangdong Provincial Hospital of Chinese Medicine, Zhuhai 519015, China.ORCID https://orcid.org/0000-0003-0469-7637
Changyu JiangMedical Research Center, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518051, China.ORCID https://orcid.org/0000-0003-4730-8988
Yongshen HeMedical Research Center, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518051, China.ORCID https://orcid.org/0000-0002-9461-6994
Lesheng HuangDepartment of Laboratory Medicine, Guangdong Provincial Hospital of Chinese Medicine, Zhuhai 519015, China.
Se PengDepartment of Laboratory Medicine, Guangdong Provincial Hospital of Chinese Medicine, Zhuhai 519015, China.ORCID https://orcid.org/0000-0002-7790-7394
Guangdong Provincial Hospital of Traditional Chinese Medicine · CNUnion Hospital · CNZhaoqing University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Serum chitinase-3-like protein 1 (CHI3L1) is a promising marker for diagnosing liver fibrosis. This meta-analysis was carried out to assess the diagnostic performance of serum CHI3L1 for the estimation of liver fibrosis. Methods: Systematic searches were performed on PubMed, Embase, Web of Science, Scopus, the Cochrane Library, Google Scholar, Sinomed, the China National Knowledge Infrastructure (CNKI), the Chinese Medical Journal Database, and the Wanfang databases for available studies. The primary studies were screened strictly according to inclusion and exclusion criteria, and sensitivity, specificity, and other measures of accuracy of serum CHI3L1 for evaluating liver fibrosis were pooled with 95% confidence intervals. Results: Our research integrated 11 articles, accounting for 1897 patients older than 18 years old. The pooled sensitivity and specificity for significant fibrosis, advanced fibrosis, and cirrhosis were 0.79 and 0.82 with an area under the receiver operating characteristic curve (AUC) of 0.85, 0.81 and 0.83 with an AUC of 0.91, and 0.72 and 0.74 with an AUC of 0.85, respectively. Random-effects models were used to assess for significant heterogeneity, and subgroup analysis showed that age and aetiology of included patients were likely sources of heterogeneity. No potential publication bias was found for serum CHI3L1 in the diagnosis of significant fibrosis, advanced fibrosis, or cirrhosis, and posttest probability was moderate. Conclusion: Measurement of serum CHI3L1 is a feasible diagnostic tool for liver fibrosis.

Indexed as

ChitinasesAdolescentChitinase-3-Like Protein 1HumansLiver CirrhosisROC CurveSensitivity and SpecificityChitinase-3-Like Protein 1Chitinases

Identifiers

PMID35360517
PMCPMC8961437
OpenAlexW4221129712

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.