Evidence map›Paper›PMID 26751220›Full record

ReviewClinical proteomics2016

Advances in mass spectrometry-based clinical biomarker discovery.

Christopher A Crutchfield, Stefani N Thomas, Lori J Sokoll, Daniel W Chan

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Clinical proteomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
116citing papers in PubMed, 1 pooled it
–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

116 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  14. Observational
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  16. Review
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  18. Article
  19. SerumACS omega · 2024
    Article
  20. Review

56 more citing papers are in PubMed but not listed here.

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

4 authors.

Christopher A CrutchfieldDepartment of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD 21287 USA ; Department of Pathology and Laboratory Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH 45219 USA.
Stefani N ThomasDepartment of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD 21287 USA.
Lori J SokollDepartment of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD 21287 USA.
Daniel W ChanDepartment of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD 21287 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The greatest unmet needs in biomarker discovery are those discoveries that lead to the development of clinical diagnostic tests. These clinical diagnostic tests can provide early intervention when a patient would present otherwise healthy (e.g., cancer or cardiovascular disease) and aid clinical decision making with improved clinical outcomes. The past two decades have seen significant technological improvements in the analytical capabilities of mass spectrometers. Mass spectrometers are unique in that they can directly analyze any biological molecule susceptible to ionization. The biological studies of human metabolites and proteins using contemporary mass spectrometry technology (metabolomics and proteomics, respectively) has been ongoing for over a decade. Some of these studies have resulted in exciting insights into human biology. However, relatively few biomarkers have been translated into clinical tests. This review will discuss some key technological developments that have occurred over this time with an emphasis on technologies that will create new avenues for biomarker discovery.

Indexed as

BiomarkerClinical performanceMass spectrometryProteomics

Identifiers

PMID26751220
PMCPMC4705754

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.