Evidence map›Paper›PMID 26659205›Full record

ReviewJournal of clinical microbiology2016

Ultrasensitive Detection and Quantification of Toxins for Optimized Diagnosis of Clostridium difficile Infection.

Nira R Pollock

Open access · bronzeAbstract readReview
In one paragraph

Review in Journal of clinical microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 16% 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

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.

  1. Guideline
  2. Article
  3. Clostridioides difficile pathogenesis and control.Nature reviews. Microbiology · 2026
    Review
  4. Article
  5. Article
  6. Novel Biomarkers, IncludingInfection and immunity · 2023
    Article
  7. A leaky human colon model reveals uncoupled apical/basal cytotoxicity in earlyAmerican journal of physiology. Gastrointestinal and liver physiology · 2023
    Article
  8. Article
  9. Rapid Quantification ofAnalytical chemistry · 2022
    Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. 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

1 author at 1 institution in 1 country.

Nira R PollockDivision of Infectious Diseases, Beth Israel Deaconess Medical Center, and Department of Laboratory Medicine, Boston Children's Hospital, Boston, Massachusetts, USA nira.pollock@childrens.harvard.edu.
Boston Children's Hospital · US

Funding

Ultrasensitive quantitative digital ELISA for C. difficile toxins in stoolR21AI103612 · NIAID · BETH ISRAEL DEACONESS MEDICAL CENTER · PI POLLOCK, NIRA · 2013 to 2014
$488k
NIAID NIH HHS R21 AI103612
6 · The paper itself

Abstract

Recently developed ultrasensitive and quantitative methods for detection of Clostridium difficile toxins provide new tools for diagnosis and, potentially, for management of C. difficile infection (CDI). Compared to methods that detect toxigenic organism, ultrasensitive toxin detection may allow diagnosis of CDI with increased clinical specificity, without sacrificing clinical sensitivity; measurement of toxin levels may also provide information relevant to disease prognosis. This minireview provides an overview of these new toxin detection technologies and considers what these new tools might add to the field.

Indexed as

Bacterial ToxinsClostridioides difficileEnterocolitis, PseudomembranousEnterotoxinsHumansImmunoenzyme TechniquesSensitivity and SpecificityBacterial ToxinsEnterotoxins

Identifiers

PMID26659205
PMCPMC4733189
OpenAlexW2191632257

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.