ArticleJournal of mass spectrometry and advances in the clinical lab2022
Listening to your mass spectrometer: An open-source toolkit to visualize mass spectrometer data.
Article in Journal of mass spectrometry and advances in the clinical lab, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- Development and Implementation of an AI System for Generating Clinical Urine Drug Test Sign-Outs.JAMA network open · 2026Article
- Clinical Pathology and the Data Science revolution.Journal of mass spectrometry and advances in the clinical lab · 2022Article
- Liquid chromatography-tandem mass spectrometry for clinical diagnostics.Nature reviews. Methods primers · 2022Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionWe have developed a set of tools built with open-source software that includes both a database and a visualization component to collect LC-MS/MS data and monitor quality control parameters. DESCRIPTION OF TOOL: To display LC-MS/MS data we built a parsing tool using Python and standard libraries to parse the XML files after each clinical run. The tool parses the necessary information to store a database comprised of three distinct tables. Another component to this toolkit is an interactive data visualization tool that uses the data from the database. There are 5 different visualizations that present the data based on interchangeable parameters. EVALUATION OF TOOL: Using histogram visualization, we assessed how quality control parameters that feed our quality control algorithm, SMACK, which assists to improve the efficiency of data review and results, performed against the collective data. Using the newly identified QC parameter values from the toolkit, we compared the output of the SMACK algorithm; the number of QC flags changed in that there was a 1.7% (31/1944 observations) increase in flags and a 7.1% (138/1944 observations) decrease in presumed false positive flags, increasing the overall performance of SMACK which helped staff focus their time on reviewing more concerning QC failures. DISCUSSION: We have developed a customizable web-based dashboard for instrument performance monitoring for our opiate confirmation LC-MS/MS assay using data collected with each batch. The web-based platform allows users to monitor instrument performance and can encompass other instruments throughout the laboratory. This information can help the laboratory take proactive measures to maintain instruments, ultimately reducing the amount down time needed for maintenance.
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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.