Evidence map›Paper›PMID 21308514›Full record

ArticleCurrent infectious disease reports2010

Early identification of sepsis.

Mateus Demarchi Gonsalves, Yasser Sakr

Abstract read
PubMed Publisher
In one paragraph

Article in Current infectious disease reports, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.2field-weighted citation impact, top 22% 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

18 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
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  4. Identifying infected patients using semi-supervised and transfer learning.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  5. Article
  6. Article
  7. Article
  8. Nanosphere's VerigeneClinical hematology international · 2019
    Article
  9. Article
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  13. Rapid antimicrobial susceptibility testing of clinical isolates by digital time-lapse microscopy.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2015
    Article
  14. Article
  15. Article
  16. Clinical application of sepsis biomarkers.Journal of anesthesia · 2013
    Review
  17. Article
  18. 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 2 institutions in 2 countries.

Mateus Demarchi GonsalvesDepartment of Anesthesiology and Intensive Care, Friedrich Schiller University Hospital, Erlanger Allee 101, 07743, Jena, Germany, mateusgonsalves@yahoo.com.br.
Yasser Sakr
Friedrich Schiller University Jena · DESchiller International University · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early diagnosis is crucial to reduce morbidity and mortality from sepsis. Clinical suspicion is the first step to diagnosis, and necessitates meticulous history taking and complete clinical examination. Special attention should be paid to identifying foci of infection. Biomarkers of host response-including acute phase proteins, procalcitonin, and various cytokines-may be useful in the diagnosis and management of patients with sepsis. Rapid and reliable detection of pathogens and their antibiotic susceptibility patterns is also of utmost importance. Many new techniques have been developed to shorten the time required for pathogen detection, including nucleic acid-based technologies (eg, polymerase chain reaction, microarrays, and hybridization). The detection of pathogen-related antigens is another approach that is useful in the diagnosis of fungal infections, targeting fungal cell wall components such as galactomannan and (1→3)-β-D-glucan.

Identifiers

PMID21308514
OpenAlexW2045627454

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.