ArticleMicroorganisms2022
Performance of a Machine Learning-Based Methicillin Resistance of
Article in Microorganisms, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled 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.
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
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
- Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2025Pooled it
- Rapid Screening of Methicillin-ResistantAnalytical chemistry · 2025Trial
- Application of machine learning with MALDI-TOF MS for rapid differentiation between methicillin-susceptible and methicillin-resistant Staphylococcus aureus.PLoS computational biology · 2026Article
- Integrating MALDI-TOF Mass Spectrometry and Machine Learning for Rapid and Clinically Relevant Differentiation of MRSA and MSSA.Pathogens (Basel, Switzerland) · 2026Article
- Combating Foodborne MRSA: Identification and Silver Nanoparticle-Based Antibacterial Strategies with Antibiotic Synergy and Resistance Evolution Assessment.Microorganisms · 2025Article
- Artificial Intelligence in Bacterial Infections Control: A Scoping Review.Antibiotics (Basel, Switzerland) · 2025Review
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- OralPathogens (Basel, Switzerland) · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Mass Spectral Imaging to Map Plant-Microbe Interactions.Microorganisms · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
The prompt presumptive identification of methicillin-resistant
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.