Evidence map›Paper›PMID 29475868›Full record

ArticleApplied and environmental microbiology2018

Establishing Statistical Equivalence of Data from Different Sampling Approaches for Assessment of Bacterial Phenotypic Antimicrobial Resistance.

Heman Shakeri, Victoriya Volkova, Xuesong Wen, Andrea Deters, Charley Cull, James Drouillard, Christian Müller, Behnaz Moradijamei, Majid Jaberi-Douraki

Open access · hybridAbstract read
In one paragraph

Article in Applied and environmental microbiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

7 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Machine Learning Techniques for Antimicrobial Resistance Prediction ofComputational intelligence and neuroscience · 2023
    Article
  6. Article
  7. 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

9 authors at 1 institution in 1 country.

Heman Shakeri *Institute of Computational Comparative Medicine, Kansas State University, Manhattan, Kansas, USA.
Victoriya Volkova *Department of Diagnostic Medicine/Pathobiology, Kansas State University, Manhattan, Kansas, USA vv88@vet.k-state.edu jaberi@vet.k-state.edu.
Xuesong WenInstitute of Computational Comparative Medicine, Kansas State University, Manhattan, Kansas, USA.
Andrea DetersInstitute of Computational Comparative Medicine, Kansas State University, Manhattan, Kansas, USA.
Charley CullVeterinary and Biomedical Research Center, Inc., Manhattan, Kansas, USA.
James DrouillardDepartment of Animal Sciences and Industry, Kansas State University, Manhattan, Kansas, USA.
Christian MüllerDepartment of Animal Sciences and Industry, Kansas State University, Manhattan, Kansas, USA.
Behnaz MoradijameiDepartment of Statistics, Kansas State University, Manhattan, Kansas, USA.
Majid Jaberi-DourakiInstitute of Computational Comparative Medicine, Kansas State University, Manhattan, Kansas, USA vv88@vet.k-state.edu jaberi@vet.k-state.edu.
Kansas State University · US

Funding

Retail Meat Surveillance in the State of Kansas as a Part of the National Antimicrobial Resistance Monitoring SystemU01FD005785 · FDA · KANSAS STATE UNIVERSITY · PI NAGARAJA, TIRUVOOR G, PALINSKI, RACHEL · 2016 to 2019
$545k
Investigating pharmacodynamics of antimicrobials against bacteria accumulating resistanceR15GM126503 · NIGMS · KANSAS STATE UNIVERSITY · PI MOSIER, DEREK · 2017 to 2017
$372k
FDA HHS U01 FD005785NIGMS NIH HHS R15 GM126503
6 · The paper itself

Abstract

To assess phenotypic bacterial antimicrobial resistance (AMR) in different strata (e.g., host populations, environmental areas, manure, or sewage effluents) for epidemiological purposes, isolates of target bacteria can be obtained from a stratum using various sample types. Also, different sample processing methods can be applied. The MIC of each target antimicrobial drug for each isolate is measured. Statistical equivalence testing of the MIC data for the isolates allows evaluation of whether different sample types or sample processing methods yield equivalent estimates of the bacterial antimicrobial susceptibility in the stratum. We demonstrate this approach on the antimicrobial susceptibility estimates for (i) nontyphoidal

Indexed as

Drug Resistance, BacterialAbattoirsAnimalsAnti-Bacterial AgentsBloodCattleCecumEscherichia coliFecesHumansMeatPhenotypeSalmonellaUrineAnti-Bacterial Agentsassessment of antimicrobial resistancebacterial antimicrobial resistancecattleEscherichia colifecal samplingNARMSSalmonellasample processingsample typesstatistical equivalence

Identifiers

PMID29475868
PMCPMC5930337
OpenAlexW2793901940

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.