Evidence map›Paper›PMID 34048021›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2021

Agent-Based Modeling of Systemic Inflammation: A Pathway Toward Controlling Sepsis.

Gary An, R Chase Cockrell

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
4.5field-weighted citation impact, top 6% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Control of medical digital twins with artificial neural networks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025
    Article
  2. Article
  3. 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

2 authors at 1 institution in 1 country.

Gary AnDepartment of Surgery, University of Vermont, Burlington, VT, USA. Gary.An@med.uvm.edu.
R Chase CockrellDepartment of Surgery, University of Vermont, Burlington, VT, USA.
University of Vermont · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite nearly 50 years of research there currently remains no mediator-directed therapy approved for the treatment of sepsis. The failure to effectively translate the copious mechanistic knowledge regarding systemic inflammation to effective therapies is a dramatic example of the translational dilemma. Dynamic computational modeling has been proposed as a vital means of integrating community-wide knowledge into an investigatory framework that allows the application of engineering-like principles to the problem of sepsis. Agent-based modeling is a computational modeling method that has been used to address some of the fundamental issues facing the sepsis research community. This chapter will introduce the rationale to augment traditional research practices with agent-based modeling, describe the basic steps in the construction and use of agent-based models, and provide examples of how the use of agent-based modeling can provide an investigatory pathway to solving the challenge of sepsis.

Indexed as

Computer SimulationHumansInflammationSepsisSignal TransductionSystems AnalysisAcute inflammationAgent based modelingArtificial intelligenceIn silico trialsKnowledge representationMachine learningMathematical modelsMultiple organ failureMultiscale modelsSepsisSystemic inflammationTranslational researchTranslational systems biology

Identifiers

PMID34048021
OpenAlexW3165672646

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.