ReviewAnimals : an open access journal from MDPI2020
Overview of Quantitative Methodologies to Understand Antimicrobial Resistance via Minimum Inhibitory Concentration.
Review in Animals : an open access journal from MDPI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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.
Who cites it
17 citing papers in PubMed.
- Article
- Unveiling the Mechanisms of Bacterial Resistance and Countermeasures.Pathogens (Basel, Switzerland) · 2025Review
- Designing of a novel chimeric antimicrobial peptide againstResearch in pharmaceutical sciences · 2025Article
- Prevalence of Variant GTRAntibiotics (Basel, Switzerland) · 2025Article
- An In Vitro Evaluation of Industrial Hemp Extracts Against the Phytopathogenic BacteriaMolecules (Basel, Switzerland) · 2024Article
- Antimicrobial Activity ofPharmaceuticals (Basel, Switzerland) · 2024Article
- Surveillance of Antimicrobial Resistance ofAnimals : an open access journal from MDPI · 2024Article
- Article
- Antimicrobial Resistance in Bacteria from Meat and Meat Products: A One Health Perspective.Microorganisms · 2023Review
- Serotype distribution and antibiogram ofMicrobiology spectrum · 2023Article
- Effect of Group Housing of Preweaned Dairy Calves: Health and Fecal Commensal Antimicrobial Resistance Outcomes.Antibiotics (Basel, Switzerland) · 2023Article
- A comparison of various feature extraction and machine learning methods for antimicrobial resistance prediction inFrontiers in antibiotics · 2023Article
- Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing ofThe European respiratory journal · 2022Article
- 3D-Printed Dip Slides Miniaturize Bacterial Identification and Antibiotic Susceptibility Tests Allowing Direct Mastitis Sample Analysis.Micromachines · 2022Article
- Implications of Veterinary Medicine in the comprehension and stewardship of antimicrobial resistance phenomenon. From the origin till nowadays.Veterinary and animal science · 2022Article
- Antimicrobial Resistance Profiles and Genes inFrontiers in veterinary science · 2021Article
- Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens.Antibiotics (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The development of antimicrobial resistance (AMR) represents a significant threat to humans and food animals. The use of antimicrobials in human and veterinary medicine may select for resistant bacteria, resulting in increased levels of AMR in these populations. As the threat presented by AMR increases, it becomes critically important to find methods for effectively interpreting minimum inhibitory concentration (MIC) tests. Currently, a wide array of techniques for analyzing these data can be found in the literature, but few guidelines for choosing among them exist. Here, we examine several quantitative techniques for analyzing the results of MIC tests and discuss and summarize various ways to model MIC data. The goal of this review is to propose important considerations for appropriate model selection given the purpose and context of the study. Approaches reviewed include mixture models, logistic regression, cumulative logistic regression, and accelerated failure time-frailty models. Important considerations in model selection include the objective of the study (e.g., modeling MIC creep vs. clinical resistance), degree of censoring in the data (e.g., heavily left/right censored vs. primarily interval censored), and consistency of testing parameters (e.g., same range of concentrations tested for a given antibiotic).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.