{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhaxi B"],"funding":["Peking Union Medical College Hospital Youth Category-D Program","Beijing Natural Science Foundation - Daxing Innovation Joint Fund"],"pagination":["288"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12839619"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(2)"],"pubmed_abstract":["<b>Background:</b> Patient-based real-time quality control (PBRTQC) enables continuous analytical monitoring using routine patient results; however, the performance of classical statistical process control (SPC) algorithms varies across analytes, and standardized evaluation and optimization strategies remain limited. To address this gap, this study compared three SPC algorithms-moving average (MA), moving quantile (MQ), and exponentially weighted moving average (EWMA)-within a unified preprocessing framework and proposed a composite performance metric for parameter optimization. <b>Methods:</b> Routine patient results from six laboratory analytes were analyzed using a standardized \"transform-truncate-alarm\" PBRTQC workflow. Simulated systematic biases were introduced for model training, an"],"journal":["Diagnostics (Basel, Switzerland)"],"pubmed_title":["Comparison of EWMA, MA, and MQ Under a Unified PBRTQC Framework for Thyroid and Coagulation Tests."],"pmcid":["PMC12839619"],"funding_grant_id":["L256080","UHB12728"],"pubmed_authors":["Zhaxi B","Ding W","Li X","Chen Q","Ma C","Hu Y","Qiu L"],"additional_accession":[]},"is_claimable":false,"name":"Comparison of EWMA, MA, and MQ Under a Unified PBRTQC Framework for Thyroid and Coagulation Tests.","description":"<b>Background:</b> Patient-based real-time quality control (PBRTQC) enables continuous analytical monitoring using routine patient results; however, the performance of classical statistical process control (SPC) algorithms varies across analytes, and standardized evaluation and optimization strategies remain limited. To address this gap, this study compared three SPC algorithms-moving average (MA), moving quantile (MQ), and exponentially weighted moving average (EWMA)-within a unified preprocessing framework and proposed a composite performance metric for parameter optimization. <b>Methods:</b> Routine patient results from six laboratory analytes were analyzed using a standardized \"transform-truncate-alarm\" PBRTQC workflow. Simulated systematic biases were introduced for model training, an","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-06-17T03:22:32.167Z","creation":"2026-06-17T03:10:39.891Z"},"accession":"S-EPMC12839619","cross_references":{"pubmed":["41594266"],"doi":["10.3390/diagnostics16020288"]}}