Evidence map›Paper›PMID 34869669›Full record

SynthesisFrontiers in cardiovascular medicine2021

The Predictive Value of Myoglobin for COVID-19-Related Adverse Outcomes: A Systematic Review and Meta-Analysis.

Chaoqun Ma, Dingyuan Tu, Jiawei Gu, Qiang Xu, Pan Hou, Hong Wu, Zhifu Guo, Yuan Bai, Xianxian Zhao, Pan Li

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 18% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Pericardial Involvement in Severe COVID-19 Patients.Medicina (Kaunas, Lithuania) · 2022
    Observational
  9. Mortality Predictors in Severe SARS-CoV-2 Infection.Medicina (Kaunas, Lithuania) · 2022
    Observational
  10. Review
  11. COVID19 biomarkers: What did we learn from systematic reviews?Frontiers in cellular and infection microbiology · 2022
    Review
  12. 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

10 authors at 3 institutions in 1 country.

Chaoqun MaDepartment of Cardiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Dingyuan TuDepartment of Cardiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Jiawei GuDepartment of General Surgery, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai, China.
Qiang XuDepartment of Cardiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Pan HouDepartment of Cardiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Hong WuDepartment of Cardiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Zhifu GuoDepartment of Cardiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Yuan BaiDepartment of Cardiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Xianxian ZhaoDepartment of Cardiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Pan LiDepartment of Cardiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Second Military Medical University · CNChanghai Hospital · CNFudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

cardiac troponin ICOVID-19mortalitymyoglobinpredictive valuesevere illness

Identifiers

PMID34869669
PMCPMC8636904
OpenAlexW3216173419

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.