Evidence map›Paper›PMID 34962616›Full record

ReviewChinese journal of integrative medicine2022

Severe Type of COVID-19: Pathogenesis, Warning Indicators and Treatment.

Ke Shi, Yao Liu, Qun Zhang, Chong-Ping Ran, Jie Hou, Yi Zhang, Xian-Bo Wang

Open access · bronzeAbstract readReview
In one paragraph

Review in Chinese journal of integrative medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 31% 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, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. 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

7 authors at 3 institutions in 1 country.

Ke ShiCenter of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
Yao LiuCenter of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
Qun ZhangCenter of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
Chong-Ping RanCenter of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
Jie HouCenter of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
Yi ZhangCenter of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
Xian-Bo WangDepartment of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100007, China. wangxb@ccmu.edu.cn.
Beijing Ditan Hospital · CNCapital Medical University · CNDongzhimen Hospital Affiliated to Beijing University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, is a major public health issue. The epidemic is unlikely to be contained until the global launch of safe and effective vaccines that could prevent serious illnesses and provide herd immunity. Although most patients have mild flu-like symptoms, some develop severe illnesses accompanied by multiple organ dysfunction. The identification of pathophysiology and early warning biomarkers of a severe type of COVID-19 contribute to the treatment and prevention of serious complications. Here, we review the pathophysiology, early warning indicators, and effective treatment of Chinese and Western Medicine for patients with a severe type of COVID-19.

Indexed as

COVID-19HumansSARS-CoV-2Chinese medicinecoronavirus disease 2019indicatorspathogenesistreatment

Identifiers

PMID34962616
PMCPMC8713541
OpenAlexW4200337969

What OpenQuestion 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 OpenQuestion graph.