Evidence map›Paper›PMID 41553751›Full record

ArticleMicrobial genomics2026

Machine learning-based lineage prediction from antimicrobial susceptibility testing phenotypes for

Theodor A Ross, Anna K Pöntinen, Einar Holsbø, Ørjan Samuelsen, Kristin Hegstad, Michael Kampffmeyer, Jukka Corander, Rebecca A Gladstone

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Microbial genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Theodor A RossDepartment of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway.
Anna K PöntinenNorwegian Centre for Detection of Antimicrobial Resistance, Department of Microbiology and Infection Control, University Hospital of North Norway, Tromsø, Norway.
Einar HolsbøCentre for New Antibacterial Strategies, UiT The Arctic University of Norway, Tromsø, Norway.
Ørjan SamuelsenCentre for New Antibacterial Strategies, UiT The Arctic University of Norway, Tromsø, Norway.
Kristin HegstadCentre for New Antibacterial Strategies, UiT The Arctic University of Norway, Tromsø, Norway.
Michael KampffmeyerDepartment of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway.
Jukka CoranderDepartment of Biostatistics, Faculty of Medicine, University of Oslo, Oslo, Norway.
Rebecca A GladstoneDepartment of Biostatistics, Faculty of Medicine, University of Oslo, Oslo, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rising antimicrobial resistance (AMR) in

Indexed as

Anti-Bacterial AgentsEscherichia coliEscherichia coli InfectionsMachine LearningBoosting Machine Learning AlgorithmsClassification AlgorithmsDrug Resistance, BacterialHumansMicrobial Sensitivity TestsPhenotypeRandom ForestUrinary Tract InfectionsAnti-Bacterial Agentsbloodstream infectionEscherichia colimachine learningST131urinary tract infection

Identifiers

PMID41553751
PMCPMC12816985

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.