ArticleJournal of mass spectrometry and advances in the clinical lab2023
Exploring
Article in Journal of mass spectrometry and advances in the clinical lab, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis 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
1 citing paper in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.
- A systematic review of machine learning on clinical MALDI-TOF MS.Briefings in bioinformatics · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Laboratory surveillance of Objectives: In this study, we aimed to investigate the viability of utilizing MALDI-TOF MS technology as an adjunctive and screening method for capsular typing of Methods: Firstly, we established a comprehensive spectral database comprising isolates of serotypes present in the PCV13 vaccine, along with the top 10 most prevalent NON PCV13 serotypes based on local epidemiological data. This database served as a foundation for developing unsupervised models utilizing MALDI-TOF MS spectra, which enabled us to identify inherent patterns and relationships within the data. Our analysis involved a dataset comprising 215 new isolates collected from nationwide surveillance in Argentina. Our approach involved developing classification models based on MALDI-TOF MS to discern between Results: Although our findings revealed suboptimal performance in serotype classification, they provide valuable insights into the potential of machine learning algorithms in this context. The sensitivity of the models ranged from 0.41 to 0.46, indicating their ability to detect certain serotypes. The observed specificity consistently remained at 0.60, suggesting a moderate level of accuracy in identifying non-vaccine serotypes. These results highlight the need for further refinement and optimization of the algorithms to enhance their discriminative power and predictive accuracy in serotype identification.By addressing the limitations identified in this study, such as exploring alternative feature selection techniques or optimizing algorithm parameters, we can unlock the full potential of machine learning in robust and reliable serotype classification of Conclusion: Overall, our study contributes to the growing body of research on utilizing MALDI-TOF MS and machine learning algorithms for serotype identification purposes.
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.