Evidence map›Paper›PMID 33647606›Full record

ArticleMedical hypotheses2021

Old drug, new Trick? The rationale for the treatment of COVID-19 with activated protein C.

Steven B Pestka

Open access · greenAbstract read
In one paragraph

Article in Medical hypotheses, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 40% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. 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

1 author at 1 institution in 1 country.

Steven B PestkaTufts University School of Medicine, Department of Medicine, Newton-Wellesley Hospital, 2014 Washington Street, Newton, MA 02462, USA. Electronic address: spestka@partners.org.
Tufts University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the COVID-19 pandemic continues, researchers seek to identify efficacious treatments. Current approaches to COVID-19 therapeutics focus on antiviral agents, convalescent plasma, monoclonal antibodies, immunomodulators and more traditional therapies such as steroids [1-6]. Reversing disturbances in coagulation has also been identified as a priority area for candidate therapies, such as through the Accelerating COVID-19 Therapeutic Interventions and Vaccines 4 adaptive clinical trial (ACTIV-4) which is currently evaluating aspirin, heparins and apixaban [7]. Since there is a clear relationship between mechanisms of coagulation and the immune response, it is possible that reversing disturbances in coagulation may diminish the dysregulated immune response observed in COVID-19. The basis for this hypothesis is described below and is followed by discussion of a proposed candidate therapy - activated protein C. By treating COVID-19 patients using a novel approach, which does not focus on immune-based or antiviral treatments, but instead which addresses both the anti-thrombotic and inflammatory consequences of infection, the hope is that new therapeutic targets can be considered and new candidate therapies, such as activated protein C, may be evaluated.

Indexed as

COVID-19 Drug TreatmentAdaptive Clinical Trials as TopicAnimalsAntiviral AgentsBlood CoagulationFibrinolytic AgentsHumansInflammationModels, TheoreticalProtein CRecombinant ProteinsSepsisAntiviral Agentsdrotrecogin alfa activatedFibrinolytic AgentsProtein CRecombinant ProteinsActivated protein CCOVID-19COVID-19 therapeutics

Identifiers

PMID33647606
PMCPMC7884230
OpenAlexW3132957046

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.