Evidence map›Paper›PMID 34240518›Full record

ReviewRespirology (Carlton, Vic.)2021

Contemporary narrative review of treatment options for COVID-19.

Lianhan Shang, David Chien Lye, Bin Cao

Open access · bronzeAbstract readReview
In one paragraph

Review in Respirology (Carlton, Vic.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 7% 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

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

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. The Effect of COVID-19 Endemicity on the Mental Health of Health Workers.Journal of the American Medical Directors Association · 2022
    Article
  7. Article
  8. Review
  9. Alamandine: Potential Protective Effects in SARS-CoV-2 Patients.Journal of the renin-angiotensin-aldosterone system : JRAAS · 2021
    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

3 authors at 2 institutions in 2 countries.

Lianhan ShangBeijing University of Chinese Medicine, Beijing, China.
David Chien LyeDepartment of Infectious Diseases, Tan Tock Seng Hospital, Singapore.ORCID 0000-0003-0324-0205
Bin CaoBeijing University of Chinese Medicine, Beijing, China.ORCID 0000-0001-6991-0350
Beijing University of Chinese Medicine · CNNational University of Singapore · SG

Funding

National Natural Science Foundation of China 81970010National Natural Science Foundation of China 82041011
6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) pandemic is ongoing and many drugs have been studied in clinical trials. From a pathophysiological perspective, anti-viral drugs may be more effective in the early stage while immunomodulators may be more effective in severe patients in later stages of infection. While drugs such as lopinavir-ritonavir, hydroxychloroquine and azithromycin have proved to be ineffective in randomized controlled trials, corticosteroids, neutralizing monoclonal antibodies, remdesivir, tocilizumab and baricitinib have been reported to benefit certain groups of patients with COVID-19. In this review, we will present the key clinical evidence and progress in promising COVID-19 therapeutics, as well as summarize the experience and lessons learned from the development of the current therapeutics.

Indexed as

COVID-19 Drug TreatmentPandemicsSARS-CoV-2Antiviral AgentsCOVID-19HumansAntiviral Agentsbaricitinibconvalescent plasmacorticosteroidCOVID-19neutralizing monoclonal antibodyremdesivirtocilizumab

Identifiers

PMID34240518
PMCPMC8446994
OpenAlexW3181664400

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.