ReviewAnnals of laboratory medicine2024
Next-Generation Patient-Based Real-Time Quality Control Models.
Review in Annals of laboratory medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Article
- A Comparative Study of the Clinical Laboratory Quality Control Performance of AI-PBRTQC and Traditional PBRTQC Model in Tumor Marker Testing.Diagnostics (Basel, Switzerland) · 2026Article
- Comparison of EWMA, MA, and MQ Under a Unified PBRTQC Framework for Thyroid and Coagulation Tests.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence in experimental and clinical in vitro analysis: applications, limitations, and future directions.Biotechnology notes (Amsterdam, Netherlands) · 2026Review
- Tribulations, Triumphs, and Governance: Shaping the Future of Artificial Intelligence in Healthcare.EJIFCC · 2025Article
- Patient-based pre-classified real-time quality control with neural network (PCRTQC-NN).Practical laboratory medicine · 2025Article
- AI Based Predictive Modelling for Internal Quality Control: A Machine Learning Approach Using Altair RapidMiner.EJIFCC · 2025Article
- Optimization of the Total Testing Process Within the Big Data-to-Big Data Loop.Annals of laboratory medicine · 2025Article
- Article
- Proposal for delta check limits of frequently requested hormones using real-world data.Biochemia medica · 2025Article
- Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact.Annals of laboratory medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.
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.