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Journal of Clinical Microbiology logoLink to Journal of Clinical Microbiology
. 2026 Feb 13;64(3):e01884-25. doi: 10.1128/jcm.01884-25

Correction for Wiesmann et al., “Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation study”

Niklas Wiesmann, Dominic Enders, Antje Westendorf, Raphael Koch, Frieder Schaumburg
PMCID: PMC12977462  PMID: 41685918

AUTHOR CORRECTION

Volume 63, no. 12, e01186-25, 2025, https://doi.org/10.1128/jcm.01186-25.https://doi.org/10.1128/jcm.01186-25. Figures 2 and 3 should appear as shown in this correction.

In Fig. 2b and 2e, the boxplots for the Klebsiella pneumoniae−Cefotaxime combination were duplicates of the boxplots for Staphylococcus aureus−Oxacillin (Fig. 2c and 2f) and were therefore incorrect. The correct boxplots are now displayed for the Klebsiella pneumoniae–Cefotaxime combination.

In Fig. 3, we mistakenly overrode the results for the Staphylococcus aureus−Oxacillin combination with the graphics and values from Klebsiella pneumoniae−Cefotaxime/Ceftriaxone. This is now corrected.

The results stated in the tables and body of the article are correct and the conclusions remain intact. We apologize for these errors, which did not affect the results or conclusions.

Fig 2.

Boxplots of benchmark results for tuned ML-models using six different learners grouped by species and antimicrobial agent.

Fig 3.

Cross-site validation of ML models. Heat maps are a visual representation of the performance (AUROC) of various learners when trained on one dataset (our own study data or DRIAMS-A) and evaluated on the respective other dataset.


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