Evidence map›Paper›PMID 32204718›Full record

ReviewCritical care (London, England)2020

Heterogeneity of Cardiovascular Response to Standardized Sepsis Resuscitation.

Fabio Guarracino, Pietro Bertini, Michael R Pinsky

Erratum issuedAbstract readReview
In one paragraph

Review in Critical care (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Article
  6. Effective hemodynamic monitoring.Critical care (London, England) · 2022
    Review
  7. Article
  8. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Fabio GuarracinoDepartment of Anesthesia and Critical Care Medicine, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy. f.guarracino@ao-pisa.toscana.it.
Pietro BertiniDepartment of Anesthesia and Critical Care Medicine, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy.
Michael R PinskyDepartment of Critical Care Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.

Funding

Machine learning of physiological variables to predict diagnose and treat cardiorespiratory instabilityR01GM117622 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI PINSKY, MICHAEL R · 2016 to 2019
$2.4M
NIGMS NIH HHS R01 GM117622
6 · The paper itself

Abstract

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2020. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

Indexed as

Cardiovascular Physiological PhenomenaEmergency MedicineHumansPlasma SubstitutesResuscitationSepsisPlasma Substitutes

Identifiers

PMID32204718
PMCPMC7092496

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.